8 Digital Tool Use and Self-Regulated Strategies in a Bilingual Online Learning… 137
8.3.1.3 F ocus Group Interview
At the completion of the Biology unit, the participants took also part in a focus
group interview led by another CLIL teacher not involved in the teaching of CLIL
science. This interview, managed by the teacher/interviewer, offered the partici-
pants an opportunity to discuss the topic free of bias towards the teacher/researcher.
The teacher/interviewer was also a German teacher, well known to both cohorts.
The group interview was chosen by the teacher/researcher firstly to provide a com-
fortable trustworthy environment for the Year 9 students and secondly to receive
input triggered by the group’s interaction, which might not have emerged in single
interviews [29]. To further support a positive and nonthreatening interview environ-
ment, the focus group interview provided the participants with the opportunity to
share their experiences while forming a mutual understanding of the questions
being posed [31]. It also acted as member check to clarify student viewpoints [29,
30] arising from the student-designed questionnaire.
8.4 Results and Discussion
8.4.1 Students’ Technology Use and Self-Regulation
The first research question concentrated on student perceptions with regard to work-
ing with the laptop in the CLIL online learning environment within scientific open
and guided inquiry processes. Student comments from the student survey and focus
group interview s offered evidence that certain software applications, particularly
the teacher-designed EdStudio and the vocabulary training website (Education
Perfect) were important to assist in their learning. The EdStudio offered the content
system [32] for all relevant Biology content information, in the form of a textbook
translations, worksheets and answer sheets, weblinks to assist with research and
simulations to clarify biology concepts or train important language functions.
Further to this, the EdStudio showed the steps of open inquiry in regard to every
topic and how students could use this approach. Students suggested that the online
learning environment allowed the students, for example, to become experts in sci-
ence by having access to the biggest library in the world; the Internet (see Table 8.2).
Data indicates students’ developed new voices, by engaging in many sources of
scientific monoglossic discourses available online. The responses to the student-
designed questionnaire highlighted that 16 of the 22 students relied heavily on the
EdStudio and the vocabulary training website. It was established by both cohorts
that the EdStudio offered a convenient content system [32], where students would
find the monoglossic German course content. It seemed significant and encouraging
for the students to know that even if they were not at school for various reasons, they
could access the information from home. The frequent comments mentioning
Education Perfect indicate that the students were actively monitoring their learning
of the German monoglossic science language as indicated in Table 8.1.
138 U. Freihofner et al.
Table 8.1 Comments on some of the ways of learning German Case 2 2015
8
Learning with technology and learning German Case 1 2014 8
8
Voluntary Learning Place users 10 7
Voluntary Education Perfect users (vocabulary 4
training website) 11
Using the laptop for learning 8
Prefer learning with technology
Table 8.2 Student comments on their laptop and use of the online learning environment
Focus group Student comments Interpretation
interview
examples Google! This student is aware that the Internet is a
Case 1: How
do you use the I pretty much use it as like, the significant source for his learning.
Internet for
learning? Internet is pretty much like the Self-efficacy of tool use is very high.
Case 2: How world’s biggest library filled with When the student utters: “the Learning
do you use
your laptop for all kinds of information. It’s also Place of course,” it is ensured that the
learning?
good because you can get teacher understands that the online learning
multiple sources for information space customized by the science teacher is
very easily seen as important learning tool to provide
Dict.cc yeah!… the monoglossic German science content.
The Learning Place of course, Mentioning the translating website dict.cc,
and things like language perfect the Education perfect site and the EdStudio
as well, because there is a bunch, (Learning Place) also confirm that the
there is a lot of tools for like student uses legitimate websites to translate
studying and all that and gather information. The student implies
that he is involved in the actual class work
and not using an automated translating
service, which would have been the case, if
only Google was mentioned.
Ah, the Learning Place, well as This student is also aware that the Learning
the student X said everything is Place is a content system; however, the link
on the Learning Place and is made to the site as a learning tool to
anything you need for your acquire German monoglossic science
lessons is on the Learning Place content. This is apparent by the last
and … even though Frau Frei, I comment directed at the science teacher,
don’t complete all of them, I still when the student says: “even though Frau
try okay? X I don’t complete all of them, I still try
okay?”
It also indicates high self-efficacy beliefs in
regard to technology use.
(continued)
Laptop uptake and engagement with the online learning environment depended
on student’s prior dispositions towards using technology. This was apparent when
students commented negatively and frequently on the perceived malfunction of the
laptops. It also showed when students had difficulties organizing their work into a
8 Digital Tool Use and Self-Regulated Strategies in a Bilingual Online Learning… 139
Table 8.2 (continued)
Focus group Student comments Interpretation
interview
examples
Case 1: What ST—oh there is [sic] many This student is expressing negative
are the things; one, it can just completely experiences with the laptop tool. He is not
challenges stop and crash on me and so any aware that the information he collects and
when you learn work, so anything you have produces could be stored on the Learning
using your worked on in the lesson you’ve Place, so that he is able to retrieve
laptop? lost and you can’t exactly get that important work. He struggled with
back easily and you have to catch organization.
back up on it, by either getting it Self-efficacy beliefs for technology use for
from a friend or copy it down learning are low.
from a friend, which is the same
thing … ah it’s not ah, yeah so,
technology, well the laptop isn’t
the most reliable thing that you
can use.
Case 2: What ST—Well, I don’t like the This student is overwhelmed with the
do you like
about the Learning Place, it is really information provided on the Learning Place
Learning Place
Studio and unorganized and confusing and and generally finds learning on the laptop
Language
Perfect? in the end I just went like rookie not rewarding.
at the Learning Place, it puts me She disengaged from the learning process
off science. Language Perfect I due to her frustration with the organization
liked it, it is just time consuming of information and files. This feeling is
and takes ages and yeah. transferred to other online learning
activities like the vocabulary training
website. Self-efficacy beliefs for
technology use are low.
customized container system [32], where lesson notes and research information can
be stored. These students felt overwhelmed with the information provided in the
EdStudio in monoglossic German science language and the Internet. This also
appeared to hinder their uptake of Education Perfect and resulted in failing to learn
the monoglossic German science language. Consequently it discouraged these stu-
dents to be open for the learning experience, curtailing the motivation to explore the
topic and therefore disengaging some students, (see Table 8.2). Students’ self-
regulatory processes and language acquisition were thus negatively affected in this
environment.
8.4.2 S tudents’ Self-Regulation in the CLIL Online Learning
Environment
The second question investigated how Year 9 students used their voices as language
and content learners to reflect on becoming self-regulated learners in the online
learning environment. Here, three categories were applied based on the three
140 U. Freihofner et al.
Table 8.3 Student uptake of self-regulation through translanguaging processes [21]
Monoglossia Interface Language for Heteroglossia
Language through
Strategies Language for learning learning learning
Language: Translanguaging:
Translanguaging Translanguaging: Translanguaging: The Drawing on
Translating Code heteroglossia to create
meshing Code- Using both scientific language is the meaning required.
switching Crossing Crossing: borrowing an
monoglossic absorbed into the out-group token, e.g.,
Self-regulation: from another genre, to
Forethought languages for everyday English adapt to a new
Planning community or identity.
Performance meaning making in an language with ease by
Reflection Reflection:
integrated system. clarifying the Cooperation with
others
Translating: understanding of the Self-evaluation
Forethought:
Monoglossic meaning German content. Self-efficacy
German language
making from one Code-switching:
language to the other. language alteration,
Code meshing: switching between two
(written languages.
translanguaging)
realization of
translanguaging in
texts.
Reflection: Reflection:
Student monitoring Peer feedback
own accuracy Forethought:
therefore help-seeking Self-efficacy content
from peers Self-efficacy
Forethought: German language
Self-efficacy
German language
language discourses of CLIL communication [18] aligned with Bakhtin’s theories
of dialogism and heterology [22, 33] (see Table 8.3). The students used translan-
guaging practices for all aspects of classroom discourses. As the students were
involved in moving between languages and discourses in their cognitive explora-
tions, they recognized that meanings beyond the taken-for-granted everyday mean-
ings could not always be applied. The “Other,” in this case the monoglossic or
heteroglossic German language, furthered the production of thought and self-
awareness [22]; it allowed the students to pause and reflect on their current knowl-
edge. Self-reflection happened through not knowing the German terms. However, if
monoglossic English science language was presented, students tended to overlook
the particular meaning of a term if it appeared to be known in a heteroglossic con-
text. The students are, for example, familiar with the heteroglossic term “open
inquiry.” Because of its familiarity, students seemed to overlook its scientific con-
text and, therefore, cognitive action by the students was not required. The following
comment from the focus group interview shows that the students were still not cog-
nizant of the scientific monoglossic English meaning, even after transparent scaf-
folding and modeling occurred during the lessons: ST (Case 1) stated “knowing
what the process of open inquiry is supposed to be, would probably be a good idea
first, because I didn’t know what that means.” Several students agreed to this
8 Digital Tool Use and Self-Regulated Strategies in a Bilingual Online Learning… 141
comment. This shows that nontechnical monoglossic science language can be taken
for granted by students and become problematic as discussed by Wellington and
Osborne [34]. However, when the students encountered either unknown German
monoglossic or heteroglossic terms, they immediately flagged this and self-reflec-
tion was set in motion. Following was the planning for strategies like translating or
code switching, and seeking peer feedback, to find an understanding as shown in
Table 8.3.
Therefore, when translanguaging is paired with students’ self-regulation strate-
gies, the following overlap can be observed. Firstly, while students negotiate two
monoglossic languages, foremost strategies of translating and code meshing seem
to take place. This strongly supports self-regulation strategies such as student moni-
toring own accuracy, and it strengthens self-efficacy belief in regard to foreign lan-
guage learning. Secondly, during the interface, the guided classroom dialogues, the
teacher, and students are allowing code switching to substitute unknown terms in
either language to successfully get meaning across. Code switching and translan-
guaging in this phase of learning motivates seeking peer feedback and boosts self-
efficacy beliefs in regard to content learning and foreign language acquisition.
Lastly, throughout the unplanned phases of student learning, when students draw on
their own heteroglossia to make sense of content, crossing between the languages
and genres facilitates cooperation with others, self-evaluation, and reinforces self-
efficacy beliefs in regard to using the foreign language in appropriate ways. Based
on these findings, educators may be able to plan online spaces and dialogic class-
room experiences that take advantages of these language strategies unearthed by the
student voices in this study. Consequently, it may be possible to facilitate the con-
scious uptake of self-regulation strategies to the repertoire of students’ learning
strategies while learning a foreign language using an online learning environment.
Thus, it can be summarized that this bilingual searching for meaning supported
various processes of self-regulated learning, like self-evaluation, self-observation,
self-efficacy, and seeking peer and teacher feedback as shown in Table 8.4.
8.5 C onclusion
Little is known about student perceptions and experiences within a CLIL and online
learning environment and how students can benefit from the educational opportuni-
ties provided. The current study addressed this gap by analyzing student conversa-
tions and comments ofYear 9 CLIL students. This student perspective has previously
been neglected in current research on educational technology use, translanguaging,
and self-regulation, but seems necessary given the student’s opinions about the
uptake of specific learning strategies and tools to enhance their learning in the CLIL
science setting.
The analysis revealed that Year 9 students classify customized online learning
spaces as content systems [32] and mostly engage to retrieve information. The dif-
ficulties arise, when Year 9 students needed to manage their own container systems
[32] applying strategies of self-regulation such as self-motivation, performance, and
142 U. Freihofner et al.
Table 8.4 Students’ translanguaging practices
Monoglossia Interface Language for Heteroglossia
Language through
Translanguaging Language for learning learning learning
Case 1
Student voice ST 1—“Is that voice ST 1—did you guys call ST 1—is broken
recordings box or windpipe?”
examples ST 2—Just put the the aorta the Aorta or the down, I don’t know,
Stimmapparat.
Case 2 (Translation: voice Hauptschlagader? unterbrechen
Student voice box)
recordings (Translation: did you (Translation: to
examples
guys call the aorta the disrupt)
Interpretation
aorta or the main ST 2—really!
artery?) ST 1—I think it’s
perfect German.
ST 2—unterbrechen;
unterverbrechen
ST 1—unter kaputt
machen
ST 2—really, that is
like kaputt machen; is
like to destroy.
ST—Zellkern, ST—Vorhof, Vorhof ST 1—the stupid
(Translation: nucleus) (Translation: atrium, computer, haben
membrane, atrium) (Translation: To have)
Zellorganellen This student is restarted.
(Translation: cell answering the teacher’s ST 2—did you just say
organelles)… questions quietly in haben (Translation: To
This student is German to himself. have) restarted?
comparing her words Zur linken, links ST 1—yes I did
with another student (Translation: To the left, something Denglisch
and identifies her left) in there … wow
missing word. ST 2—I did it once.
Translanguaging: Translanguaging: The Translanguaging:
Drawing on
Using both scientific language was heteroglossia to create
the meaning required.
monoglossic German absorbed into the
and English science everyday English
languages for meaning language with ease by
making. clarifying the
understanding of the
German content.
Self-regulated Self-regulated Learning: Self-regulated
Learning: Student Peer feedback Learning: Cooperation
monitoring own Self-efficacy content with others
accuracy therefore Self-efficacy German Self-evaluation
help seeking from language Self-efficacy German
peers language
Self-efficacy German
language
8 Digital Tool Use and Self-Regulated Strategies in a Bilingual Online Learning… 143
Table 8.5 Vocabulary training website score comparison
Students 2014 Score out of 88 Q’s Correct % Attempts Total questions learnt
1 103 86 388 99
2 89 82 326 83
3 57 89 204 34
4 47 79 183 47
5 37 71 206 23
6 32 91 109 30
7 16 61 84 15
8 11 54 68 11
9 1 67 1
10 100 9 1
11 1 4 0
Students 2015 0 Total questions learnt
1 Score out of 88 Q’s Correct % Attempts 224
2 732 99 1718 102
3 187 77 765
4 129 85 405 90
5 26 93 21
6 12 85 75 7
7 29 73 41 20
8 70 72 116 69
9 2 100 342 8
10 46 78 15 24
11 0 180 0
2 2
88 8
volitional control. A container system according to Steffens [32] is a customized
online learning space used to manage learning and information independently by
the learner. This system received the highest rating for self-regulated learning abil-
ity in a study with university students, as a container system requires the learner to
be active and independent in their learning journey [32]. The Year 9 students’ diffi-
culties with this system showed in student comments revealing that self-motivational
and self-efficacy beliefs appear to influence student uptake of laptop and online
learning environment use. However, an important change in student uptake of tool
use occurred through the introduction of a domain-specific-guided interactive simu-
lation with feedback function. It is interesting to note that the vocabulary training
website score showed a clear difference between 2014 and 2015 in regard to learned
questions. In 2014, the website only offered vocabulary training in four modes—
reading, writing, dictation, and listening. In 2015, the guided simulation feature
called smart lesson was introduced and quizzes, close exercises, and a competition
were added. The students engaged more and the results were markedly improved.
This data suggests that an interactive online simulation with structured feedback is
a relevant factor to improving student tool use. This finding is supported by research
from De Jong [35] who argued that scaffolding in inquiry simulation is necessary
144 U. Freihofner et al.
for student success and by other researchers who established that simulations work
best with inbuilt feedback functions [36]. The findings clearly showed a positive
uptake of tool use related to the introduction of the guided simulation in 2015 (see
Table 8.5).
From the comments of the focus group interviews, it can be summarized that
Year 9 students are not cognizant of their learning strategies. Even though transpar-
ent scaffolding and modeling were provided for scientific open inquiry in the online
learning environment, the students in this CLIL classroom setting did not seem to
take notice of the processes of open inquiry and connected self-regulated learning
strategies. These findings stand in contrast to current research stating that appropri-
ate online scaffolding combined with human support could lead secondary students
to take up self-regulated processes and open inquiry strategies [10, 13, 14, 36, 37].
A further key point in the findings relates to the student’s translanguaging strate-
gies linked to self-regulated learning. It is highly plausible that students benefit from
the translanguaging practices in the CLIL environment, affording students more
access to self-regulatory strategies such as self-motivation, performance, and self-
evaluation. This supports students’ development of self-efficacy beliefs and seeking
feedback. Through self-evaluating translanguaging processes, students show deeper
cognitive processes by rethinking meanings they may have taken for granted if they
were delivered in their native language. This is reinforced by research from Garcia
and Wei [21] as well as Blackledge and Creese [38] who found that translanguaging
builds deeper thinking and additionally develops language and literacy skills [21,
38]. The pause created by rethinking meanings allows the students to realize that
their language choices are not yet correct. It alerts the students that their current
language knowledge is still developing and the content may not be understood,
therefore feedback is required or new research has to occur. Consequently, these
translanguaging practices establish a connection between students’ use of self-reg-
ulation and open inquiry. For educators, it might be possible to integrate student
activities into their design of online bilingual learning spaces that might further self-
regulated learning strategies such as a positive uptake of laptop tool use combined
with a conscious choice to monitor accuracy of knowledge related to task parame-
ters (standards for success) and self-p arameters (own interest and effort).
It should be noted that this current investigation had its limitations by being situ-
ated in a unique CLIL environment where students were exposed to a triple chal-
lenge. The learning involved the negotiation of a bilingual setting, new laptop tool
use, and a new online learning environment. Hence, the findings are significant for
a CLIL setting, where these challenges exist and highlight the importance of careful
customization of online learning environments and software applications. In sum-
mary, two practical considerations emerge from this study. Firstly, Year 9 students
in a CLIL setting are more likely to engage in a guided learning approach in an
online learning environment. This is in line with the student’s preference for guided
online simulations. It might be feasible to include automated feedback functions
and alerts into the design of online learning spaces to offer choices for students to
independently navigate through the steps of open inquiry, while they research and
learn new science content knowledge.
8 Digital Tool Use and Self-Regulated Strategies in a Bilingual Online Learning… 145
Secondly, the translanguaging practices in the CLIL setting appear to be benefi-
cial to student’s development of self-regulation strategies. Future research into spe-
cific customized online learning environment designs, including guided simulations
to further the uptake of self-regulation and language acquisition, based on the
accommodation of student opinion and perceptions would provide further inside
into the success to deliver strategies for self-regulation, translanguaging, and open
inquiry processes in online learning environments.
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Part III
Measuring and Assessing Teaching and
Learning with Educational Data Analytics
Chapter 9
Evaluation of Leaning Unit Design
Using Page Flip Information Analysis
Izumi Horikoshi, Masato Noguchi, and Yasuhisa Tamura
Abstract In this chapter, the authors attempted to evaluate design of leaning units
using learning analytics techniques on page flip information. Traditional formative
assessment is carried out by giving assignments and evaluating their results; how-
ever, the information the teacher can get from this method is limited and coarse-
grained. The authors set a research question whether one can evaluate the relation
between specific learning objectives of learning units and the learners’ actual activi-
ties in the units based on page flip histories. The experimental results showed that
the intensity of relations between the assignments and learning materials was differ-
ent for each unit; quantitatively, the correlation coefficients between “p-value of
chi-squared tests between range of page flip count and grade on assignments” and
“ratio of number of questions in an assessment with reference pages” was −0.889.
With this relation established, the authors attempted to evaluate unit design and
found that the critical factor for getting a high grade was how many times they
referred to their textbook, although this assignment was designed to assess learners’
ability to use or apply learned knowledge. If the teacher can get such suggestions as
feedback, it is possible to utilize them for formative assessment.
9.1 I ntroduction
9.1.1 D igitization in Education and Learning Analytics
In the age of paper-based learning environments, learning records were limited in
respect of both quantity and variety, usually consisting only of results of exams,
grades on assignments, and course histories. In the 1990s, with the widespread
I. Horikoshi (*) 149
Graduate School of Science and Technology, Sophia University, Tokyo, Japan
e-mail: [email protected]
M. Noguchi · Y. Tamura
Faculty of Science and Technology, Sophia University, Tokyo, Japan
© Springer International Publishing AG 2018
D. Sampson et al. (eds.), Digital Technologies: Sustainable Innovations for
Improving Teaching and Learning, https://doi.org/10.1007/978-3-319-73417-0_9
150 I. Horikoshi et al.
introduction of computers into schools, learning record data were moved into
machine-readable form, and in the 2000s, learning management systems (LMSs)
have spread, and many types of learning activity logs came to be collected. Finally,
in the 2010s, usage of laptop or tablet computers has become common even in K–12
education, enabling the recording or collection of various fine-grained learning
activity logs. These modern logs often record client-side activities, such as page
flips, answering process, eye tracking, voice data and ambient sounds, and GPS
information. In the future, even physiological data like blood pressure, sweating, or
heartbeat may be treated as “learning activity logs” and used to inform educational
theory and practice.
As part of this trend toward digitization in education, learning analytics (LA) has
become a major area in learning science and learning technology research. Ferguson
[1] defined learning analytics as follows: “Learning analytics is the measurement,
collection, analysis and reporting of data about learners and their contexts, for pur-
poses of understanding and optimizing learning and the environments in which it
occurs.” (p. 3). Ferguson does not mention to whom these data are reported in her
definition of LA; however, it can be assumed that different users of the data, for
example, learners, teachers, or schools, will have different needs—for example,
learners may want to know their own grade or position in their class, while teachers
want to know whether their lectures worked as intended.
9.1.2 Learning Analytics and Formative Assessment
In this chapter, the authors focus on utilizing LA for formative assessment, in other
words, focus on LA for teachers. Bloom [2] defined formative assessment as fol-
lows: “the use of systematic evaluation in the process of curriculum construction,
teaching and learning for the purpose of improving any of these three processes”
(p. 117).
Traditionally, formative assessment has been carried out by giving assignments
or quizzes and evaluating their results. However, assignments or quizzes are not
administrable more than once a unit, and, therefore, the information they provide
teachers is limited and coarse-grained. Therefore, in order to obtain more detailed
information, teachers need to observe their learners carefully. It is difficult to gather
information in this way on what they did or said, much less what they thought or
what they learned. In the field of cognitive science, many methods have been devel-
oped to estimate such cognitive information in a fine-grained way; however, they
require qualitative assessment, and are also labor intensive, adding to teachers’
workload.
However, as mentioned in Sect. 9.1.1, fine-grained learning data can also be
recorded in machine-readable form. In LMS-based environments, some kinds of
LA are already at practical stage. However, as mentioned in Sect. 9.1.1, fine-grained
9 Evaluation of Leaning Unit Design Using Page Flip Information Analysis 151
learning data can also be recorded in machine-readable form. In LMS-based envi-
ronments, some kinds of LA are already at practical stage. For example, Moodle,
which is one of the popular LMS, has a function to accumulate the learning history
log, and teachers and administrators can see the report [3]. Activities seen from the
report function are, for example: time stamp, student’s ID, which activity or resource
he/she clicked, and their IP address. This “Logs” is simple function to show raw
data of learning history; however, Moodle has more sophisticated functions:
“Grades,” “Competencies,” “Activity completion,” “Course completion,” “Course
reports,” and so on. Furthermore, according to Scapin [4], Moodle also has several
plugins related to Learning Analytics. For example, SmartKlass [5] provides learn-
ing dashboard, and it can help teachers to identify the students lagging behind or the
students that content is not challenging enough for them.
One aspect of learning design that the authors have focused on is the unit’s learn-
ing objective—often divisible into acquisition vs. application of knowledge or the-
ory. In Japan, the Ministry of Education, Culture, Sports, Science and Technology
(MEXT) conducts a yearly National Assessment of Academic Ability (NAAA) in
mathematics and language for Grades 6 and 9. According to the Organisation for
Economic Co-operation and Development [6], the NAAA consists of two types of
assessment, namely, assessment of the learner’s learning (subject knowledge; prob-
lem Type-A) and of the learner’s achievement (practical use of knowledge and
skills; problem Type-B). Shirouzu et al. [7] mentioned that “problem Type-B” in
language roughly corresponds to TIMSS or PISA literacy problems.
9.1.3 Page Flip History of Digital Textbook
Among the various data types that feature in learning analytics, the authors have
focused on “page flip” logs of learning materials. A page flip log or page transition
log captures when and in what order the learner flipped pages—the “page flip his-
tory,” which is not available from paper-based textbooks or other materials. This
means that teachers using paper-based textbooks need to conduct lessons on the
assumption that the learners follow course of instruction, whereas, if teachers use
digital textbooks running on client PCs, the page flip history of each learner can be
collected and visualized.
Many studies focus on page flip history. Nicholas et al. [8] reported analysis of
transactional logs obtained from the MyiLibrary platform for 127 UK universities.
In Japan, Kyushu University has carried out a whole-university project to collect
and analyze learning data from its campus LMS (Moodle) and an e-book system
(BookLooper) [9]. The objectives were as follows: (1) improve learning materials,
(2) analyze learning patterns, (3) detect students’ comprehensive level, (4) predict
final grades, and (5) provide personalized recommendation of e-books [10].
152 I. Horikoshi et al.
9.1.4 Research Question
The purpose of this research is to verify that one can evaluate the design of learning
units based on their page flip history and, in particular, whether one can evaluate the
relation between units’ learning objectives and learners’ actual activities in the
units. If this relation can be visualized and evaluated, it will facilitate quantitative
formative assessment from these fine-grained data.
9.2 M ethods
9.2.1 T arget Courses, Units, and Participants
The authors set two target courses, both held at Sophia University, Japan, and one
of the authors was in charge. Target courses, units, and number of participants are
shown in Table 9.1.
The Information Literacy course was an entry-level course for first-year students,
so the content was rather basic; in contrast, the Learning Technology course was for
third-year students, so the content was relatively advanced.
9.2.2 D ata Acquisition
As previously reported [11], the authors focused on page transition in PowerPoint
slides, because one author (Tamura) mainly uses PowerPoint in his lectures. Another
author of that paper (Yamazaki) developed the data acquisition scheme and the
function to detect and transfer page flip logs automatically. This function was imple-
mented in JavaScript, and, therefore, the authors converted the original materials
(PowerPoint files) into HTML and JPEG files and added the JavaScript. Page flip
logs and other information (date and time, student’s ID and page number) were sent
to a learning record store (LRS) when the participant changed pages.
Table 9.1 Information on target unit
Course Unit ID Unit Date Number of participants
Information IL518 Journal search May 18, 2015 71
Literacy IL601 Numerical data June 1, 2015 64
Learning LT519 Instructional design May 19, 2015 37
Technology LT526 Test and feedback May 26, 2015 34
9 Evaluation of Leaning Unit Design Using Page Flip Information Analysis 153
9.2.3 P rocedure
In the target classes, the author (Tamura) held lectures as usual using the target
materials. Participants accessed the materials, and page flip logs were transferred
into the LRS, when each participant changed pages as needed. Then, learners were
given an assignment based on the reviewed materials.
9.3 Result
9.3.1 P age Flip History and Page Flip Count
An example of page flip history is shown in Fig. 9.1. The vertical axis shows
(PowerPoint slide) page number and the horizontal axis shows time (maximum of
90 min); the thick line shows the teacher’s page flip history and the thin lines those
of the participating students.
“Page flip count” is the number of page transitions to a new page (e.g., excluding
clicking on a link to the same page in error).
Fig. 9.1 Example of page flip history (unit LT526)
154 I. Horikoshi et al.
9.3.2 Correlation Between Page Flip Count and Assignment
Grade
At the beginning of the analysis, the authors determined the strength of the correla-
tion between page flip count and the learner’s grade achieved on the assignments for
that unit, to investigate the possibility that learners who refer to their textbook often
are studying more diligently and will get a higher grade as a result. Results for page
flip count, grade, and correlation coefficient between them for the four units are
shown in Table 9.2 and a scatter plot in Fig. 9.2.
Table 9.2 Correlation Unit ID Correlation coefficient p-value
coefficients between page flip
count and grade on IL518 0.122 0.360
assignments IL601 0.195 0.142
LT519 −0.142 0.409
LT526 −0.207 0.263
40 40
grade on assignment 20 20 grade on assignment
00
-20 -20
0 20 40 60 80 100 0 10 20 30 40
40
page flip count page flip count
(a) IL518 (b) IL601
0
grade on assignment 20 0 grade on assignment
0 20 40 60 0
-20 page flip count 0
-40 (c) LT519 0 10 20 30 40 50 60
0
page flip count
(d) LT526
Fig. 9.2 Scatter plot of page flip count vs. grade on assignment
9 Evaluation of Leaning Unit Design Using Page Flip Information Analysis 155
Fig. 9.3 Page flip history for each learner (ordered by grade, LT526)
As reflected in Table 9.2 and Fig. 9.2, there was no significant correlation
between page flip count and grade on assignments. This means that no matter how
much learners referred to their textbook, they did not improve their grade. This is
visualized in Fig. 9.3, which shows the page flip history of each learner for one unit
(LT526), ordered by grade from upper left to lower right (the history at top left is the
teacher’s). Blank cells represent learners who were absent from the class. For exam-
ple, the learner in the third cell from the right in the top row is the second in grade
of assessment while he or she was absent from the class.
Looking at Fig. 9.3, focusing not only on the page flip count but also on the page
transition pattern, students can be divided into the following four types depending
on the combination of page transition pattern and grading tendency:
• Type-1: Obedient & high grade
• Type-2: Independent & high grade
• Type-3: Obedient & low grade
• Type-4: Independent & low grade
Students of Type-1 were obedient. Their histories are similar to teacher’s history.
In other words, they followed teacher’s page flip. Also, students of Type-3 were
obedient. Both Type-1 and Type-3 students followed teacher’s page flip; however,
Type-1 students got high grade but Type-3 students got low grade in their assign-
ment. This means, some students viewed textbook almost same as teacher, but got
low grade. From these results, it is suggested that obedience is not the necessary
condition for high grade, and it is also suggested that obedient does not necessarily
get good grades. In general, teacher often instructs students with low grades that
“Open the page of the text I am explaining and listen carefully.” However, this
instruction may not be effective at least in the classes we analyzed this time.
156 I. Horikoshi et al.
Table 9.3 Chi-Squared test Unit ID chi-squared p-value
between range of page flip
count and grade on IL518 28.19 0.000
assignments IL601 14.26 0.027
LT519 8.34 0.214
LT526 4.58 0.599
9.3.3 Chi-Squared Test Between Range of Page Flip Count
and Grade on Assignments
As mentioned in Sect. 9.3.2, there was no significant correlation between page flip
count and grade on the assignments; therefore, we next verified whether there was
significant variation in grade by range of page flip count. This verification was per-
formed using cross-tabulation and independent chi-squared test. For cross-
tabulation, page flip counts and learner grades were divided into ranges as below:
Page flip count
• Range A: less than teacher’s flips
• Range B: more than teacher’s flips and less than twice teacher’s
• Range C: more than twice teacher’s
Grade on assignments
• Range 1: more than the first quartile
• Range 2: more than the median
• Range 3: more than the third quartile
• Range 4: less than the third quartile
The results of the chi-squared test of independence based on the cross-tabulation
are shown in Table 9.3, where significant variation is revealed in grade by range of
page flip count in IL518 and IL601, but not in LT519 or LT526. This result possibly
arose from the difference between two courses (IL and LT). However, there are still
differences even within the same course: between IL518 and IL601 and between
LT519 and LT526.
9.3.4 Relation Between Assignments and Learning Materials
Based on the results presented in Sect. 9.3.3, the authors hypothesized that the
intensity of the relation between assignments and learning materials is differ-
ent for each unit and that the p-value of the chi-squared test presented in Sect.
9.3.3 reflects this intensity. As mentioned in Sect. 9.1.2, the learning objectives
9 Evaluation of Leaning Unit Design Using Page Flip Information Analysis 157
of some units involve acquisition of knowledge or theory, and of other units, their
application; for simplicity, the authors refer to the former as Type-A and the lat-
ter as Type-B. Corresponding to the unit types, assignments for Type-A units are
designed to assess whether learners have acquired the knowledge presented in
class; for this purpose, it would be natural for them to refer to the learning mate-
rials. In contrast, assignments for Type-B units are designed for assessment of
learners’ skills and ability to use or apply the learned knowledge. Thus there may
not be reference pages in the textbook which correspond directly to the question.
In this case, it may be less important for students’ grades how many times they
refer to their textbook.
As implied above, our hypothesis consists of two elements: (a) Whether some
questions are associated with specific reference pages and others not, and the ratio
of number of questions in assessments thus differs by unit, and (b) whether there is
a correlation between the “p-value of chi-squared tests between range of page flip
count and grade on assignments” and “ratio of number of questions in an assess-
ment with reference pages.”
First, the authors examined each assignment to verify (a) and evaluated ratio of
number of questions in an assessment with reference pages. The results were as fol-
lows: IL518 (20%), IL601 (10%), LT529 (5%), LT526 (0%). Ratio of number of
questions in an assessment with reference pages is given in parentheses. This result
demonstrated that the ratio of number of questions in an assessment with reference
pages differs by unit, verifying (a). Therefore, the authors then determined the cor-
relation coefficients between the “p-value of chi-squared tests between range of
page flip count and grade on assignments” and the “ratio of number of questions in
an assessment with reference pages,” to verify (b). The scatter plot for this result is
shown in Fig. 9.4; the correlation coefficients was −0.889, indicating a strong nega-
tive correlation and verifying (b).
p-values of chi-squared tests 0.800 5 10 15 20 25
between range of page flip count 0.600
0.400 ratio of number of questions
and grade on assignments 0.200 with reference pages (%)
0.000
0
-0.200
-0.400
Fig. 9.4 Scatter plot of “p-value of chi-squared test between range of page flip count and grade on
assignments” and “ratio of number of questions with reference pages”
158 I. Horikoshi et al.
9.4 Discussion
The result in Sect. 9.3.4 demonstrates that the intensity of relation between assign-
ments and learning materials is different for each unit. Also, Fig. 9.4 and the corre-
lation coefficients in Sect. 9.3.4 show that the larger the ratios of questions that have
reference pages in a unit are, the more significant the p-values of chi-squared
tests are. That is, as mentioned in Sect. 9.3.4, the intensity of the relation between
range of page flip count and grade on assignments reflects the intensity of the rela-
tion between the assignments and the learning material. Based on these results, it
seems possible to provide formative assessment based on page flip history, that is,
to evaluate based on the intensity of this relation whether the lectures or assign-
ments worked as the teacher or designer intended.
The intensity of the relation between the assignments and the learning material
is quantitatively represented as the “ratio of number of questions in an assessment
with reference pages,” and it may reflect learning design intention as discussed. By
contrast, the intensity of the relation between range of page flip count and grade on
assignments is quantitatively represented as the “p-value of the chi-squared test
between range of page flip count and grade on assignments,” and it may represent
how the lecture or assignments actually worked. If these two intensities are compa-
rable, this may appear as a negative correlation coefficient.
Therefore, in order to evaluate a certain unit in terms of formative assessment
based on these correlation coefficients, the authors should focus on whether a point
in the scatter plot shown in Fig. 9.4 is on the regression line. For example, as can be
observed in Fig. 9.4, the point in IL601 is an outlier. This shows that the “ratio of
number of questions in an assessment with reference pages” is small in this unit,
although the intensity of the relation between range of page flip count and grade on
assignments is nevertheless strong. In this case, getting a high grade on the assign-
ments may depend on how many times one refers to one’s textbook, even though
this assignment was designed to assess learners’ ability to apply learned knowledge.
If the teacher can get this suggestion as feedback, it is possible to utilize it for for-
mative assessment; for example, a teacher can improve the ability of his/her assign-
ments to assess learners’ applied skills.
9.5 Conclusion and Future Works
The results of this experiment demonstrated that the intensity of relation between
assignments and learning materials are different across units with different content.
The correlation coefficient between the p-value of chi-squared test and versus ratio
of number of questions in an assessment with reference pages was −0.889. Using
this relation, the authors attempted to evaluate the design of the learning units and
found that an assignment for a certain unit was designed to assess learners’ ability
to use or apply the learned knowledge, but that this may depend on how often stu-
dents refer to their textbook.
9 Evaluation of Leaning Unit Design Using Page Flip Information Analysis 159
If the teacher can get such suggestions as feedback, it is possible to utilize them
for formative assessment. As this implies, the authors verified the hypothesis that
the authors can evaluate the design of a unit from its page flip history, especially as
regards the relation between the learning objective of the unit and the learner’s
actual activity in the unit. This shows more broadly that this approach enables quan-
titative formative assessment from fine-grained data, with a lighter workload.
The authors conclude by mentioning some future work that takes this chapter as
a starting point. In this chapter, page flip log data was used to gather page flip count.
However, the original page flip log data also contains information on sequence and
timespan. Thus, a more extensive analysis of data is needed that also considers these
temporal aspects. For example, it may be possible to estimate which pages learners
spend time reading and which pages skipped.
Acknowledgments This work was supported by JSPS KAKENHI Grant Number 26282059. We
thank Mr. Kimiaki Yamazaki of TEK Software for developing the page flip acquisition scheme.
References
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an alternative framework for the assessment of collaborative problem solving. Proceedings of
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ac.jp/e/.
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dents for e-book-based learning analytics. Proceedings of Cross-LAK 2016. Edinburgh, UK,
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Chapter 10
Exploring Adaptive Game-Based Learning
Using Brain Measures
Jelke van der Pal, Christopher Roos, and Ghanshaam Sewnath
Abstract The prospective use of low fidelity simulation and gaming in aviation
training is high and may facilitate individual, personal training needs in usually
asynchronous training setting. Without direct feedback from, or intervention by, an
instructor, adaptivity of the training environment is in high demand to ensure train-
ing sessions maintain an optimal training value to the trainee. In game design the-
ory, the flow principle is used to provide an optimally engaging experience, whereas
its equivalent in instructional design theory is maintaining the optimal cognitive
load by adjusting the task complexity or by scaffolding. The control of these prin-
ciples can be based on user activity or performance. Alternatively, brain measures
may be used to control the learning experience of professionals. This chapter
explores the options for using brain measures for professional gaming and provides
results of a pilot study. Based on the pilot study, it is concluded that brain measures
may be a viable but demanding mechanism for optimizing the learning process.
10.1 Introduction
Aviation has a long history of using simulation for training purposes. In particular,
the yearly recurrent and compulsory training for pilots is provided in mostly high-
end simulators. There is growing insight that the standard curriculum is in need of
revision. A wider use of training media may be required to ensure more training
goals are covered in a better way while addressing more personal needs. PC-based
simulation and game-based learning are considered candidates that partly replace
This chapter is an extended version of the paper “Adaptive Game-Based Learning Using Brain
Measures for Attention—Some Explorations” presented at the 13th International Conference on
Cognition and Exploratory Learning in Digital Age (CELDA 2016).
J. van der Pal (*) · C. Roos · G. Sewnath
Netherlands Aerospace Centre, Amsterdam, The Netherlands
e-mail: [email protected]; [email protected]; [email protected]
© Springer International Publishing AG 2018 161
D. Sampson et al. (eds.), Digital Technologies: Sustainable Innovations for
Improving Teaching and Learning, https://doi.org/10.1007/978-3-319-73417-0_10
162 J. van der Pal et al.
and partly extend training on high-end simulators. As training time is very expen-
sive and personal needs require flexible solutions, a considerable part of new train-
ing options will require unscheduled and mobile activity, in which the pilot is more
in control of what, how, and when to train. This may lead to new organizational and
regulatory mechanisms to register and accredit training as well as new instructional
techniques to support the personal training needs without immediate availability of
an instructor. Without direct feedback from, or intervention by, an instructor, adap-
tivity of the training environment is in high demand to ensure training sessions
maintain an optimal value to the trainee. This chapter first explores overall require-
ments for adaptive training and then focuses on the use of a specific technique: brain
computer interfacing.
10.1.1 I ngredients for Adaptive Training
A core element of effective training as well as effective games is the management of
cognitive load and motivation. In game design theory, the flow principle is used to
provide an optimally engaging experience, whereas its equivalent in instructional
design theory is maintaining the optimal cognitive load [1]. Good game experience
requires the player to be in a “flow” state of mind [2] which is feeling competent but
challenged while being immersed in the game. This requires game designers to
build up game events and levels that are neither too easy (boring) nor too difficult
(frustrating) while ensuring that challenging periods are balanced with more relax-
ing periods without losing the players’ attention. Instructional design sequencing
principles have similar goals which are achieved by increasing the task difficulty
and by scaffolding principles (supporting or automating part of the tasks, such as the
trainer wheels for learning to ride a bicycle); see Van Merriënboer and Kirschner
[3]. Adaptive training regulated by combining measures of performance and mental
effort has shown to accelerate the learning curve for Air Traffic Control [4] and
Flight Management System training [5]. The learner’s cognitive load, i.e., the num-
ber of elements in working memory that need to be processed simultaneously dur-
ing a learning process [6], needs to be in an optimal band to ensure efficient learning.
Over- or under-stimulation leads to frustration or boredom and results in inefficient
or even ineffective training. The principles of flow and the optimization of cognitive
load both aim to control learning and motivation, although they differ in which
technique is applied. In gaming, the focus is centered on experience, whereas the
focus in training is on performance.
In training (studies), cognitive load is usually controlled by measuring (self-
rated) load and performance after completion of a learning task. For aviation train-
ing to be fully effective, the events need to adapt to the cognitive load of events
within a training session. Performance measured during the simulation or game is
labeled as in-game measurement or stealth assessment [7], techniques that require a
coherent assessment framework, a user model, and considerable further research
and development [8] before well-grounded and practical use for automated adaptive
10 Exploring Adaptive Game-Based Learning Using Brain Measures 163
training is achieved. Real-time measures of mental states that reflect the experience
of cognitive load (such as attention, engagement, situation awareness, and bore-
dom) should be part of such a framework.
Psychophysiological indicators for cognitive load that allow for noninvasive,
real-time measurement include heart rate (HR), heart rate variability (HRV), elec-
troencephalogram (EEG), electrocardiogram (ECG), galvanic skin response (GSR),
pupil diameter, eye movements, blinking, and respiration. Studies comparing these
measures often do not lead to comparable results on the validity of the measures [9].
This may relate to the different tasks provided and to different measurement tools
and analysis techniques applied. Haapalainen et al. also show the relevance of creat-
ing a personalized cognitive load classifier for each user. This will require some data
for each user to train the classifier and select the best measure(s) per person before
the actual measurements start.
Often, cognitive load indicators require tooling and techniques that tend to be
intrusive, i.e., influence the task execution, and do not support real-time analysis.
In this chapter, we focus on a brain measures that are relatively nonintrusive and
have the potential to be an instrument for controlling the learning process by
automatically adjusting events in the learning scenario to ensure an optimal
learning experience.
10.1.2 U sing BCI for Adaptive Training
Brain Computer Interface (BCI) stands for a range of techniques that support the
brain to control a device without using muscles. For adaptive training, the trainee
does not control the training tasks, events, setting, or feedback by active thought or
sheer willpower (which is known as active BCI), but in a more indirect and involun-
tary way (also known as passive BCI), based on the measured amount of attention
or relaxation, engagement or drowsiness, or other relevant mental states. BCI tech-
niques for real, daily training will require a noninvasive, easy-to-use device. Wireless
EEG devices with dry sensors may be candidates for practical BCI. There are sev-
eral commercial EEG devices on the market that seem to be suitable. A range of
validation studies have revealed some application areas as well as the limitations of
these “simple” devices [10, 11].
EEG (electroencephalography) is a well-known technique to measure the
electric activity of the brain (groups of neurons firing simultaneously) on the
scalp. After removal of, e.g., muscle generated artifacts, EEG contains oscilla-
tions of various frequencies (from 0.1 to 100 Hz) and amplitudes (up to 200 μV).
These vary as a result of processing sensory input and internal mental activity. As
a result, EEG is different on the various parts of the brain, although precise loca-
tion is not a strong feature of EEG. The different frequencies have been found to
indicate certain mental states and emotions. For example, a low amplitude in the
region of 8–12 Hz indicates attention, especially in combination with a high
amplitude in the 13–30 Hz range. The frequency bands have been labeled by
164 J. van der Pal et al.
Table 10.1 Preprocessed Neurosky Mindwave Emotive Insight
EEG measures in commercial Meditation
EEG devices Relaxation
Attention Interest/Affinity
Focus
Engagement
Instantaneous excitement
Long term excitement
Greek letters (alpha to gamma) and include sub-bands like low and high beta.
The (sub-)bands and certain composite measures indicate a variety of mental
states and functions. An example of a composite measure is the Task Engagement
Index, calculated by beta/(alpha + theta), which has been constructed for adap-
tive automated flight control [12] and has for instance been applied in measuring
immersion during game play [13].
Raw EEG data is normally recorded for later analysis, which requires powerful
computers, complex algorithms, and time. BCI cannot work this way, as specific
EEG frequencies or indexes need to be calculated and corrected for muscle activity
in real time. This requires a highly dedicated algorithm tuned to the specific sensors
and locations, hardwired into a small chip in the device itself. This in turn demands
considerable research and development, and the companies consequently consider
the results as proprietary, including basic information on the frequency bands or
composite measures used. A number of currently available BCI measures are pre-
sented in Table 10.1.
For the concepts of flow and optimal cognitive load, EEG indicators for cogni-
tive load/task difficulty, attention, and task engagement are relevant. Neurosky’s
attention measure, for example, may be used to control task difficulty as indicated
by Fig. 10.1. When the attention level is above a certain threshold, it triggers a less
difficult task; when attention level is below a certain threshold, the task becomes
more difficult.
Task difficulty has been found to associate with theta and alpha oscillations.
Theta (which is most prominent in the frontal midline) is increased in high difficulty
tasks in flight simulators [14]. Alpha indicates the cognitive load of visual/auditory
tasks. For military pilots, alpha is found to decrease during demanding air refueling
and landing exercises [15]. There are indications that the high alpha band is more
related to (verbal) long-term memory activities and theta to working memory [16].
High theta power is, therefore, a candidate trigger to control overstimulation, and
high alpha power is a candidate trigger to control under-stimulation in a scenario.
As a first exploration, the attention indicator of the Neurosky Mindwave will be
used as this measure has a build-in calibration procedure that leads to a 1–100 scale
for each person tested. One indicates the lowest personal attention level and 100 the
highest personal attention level.
10 Exploring Adaptive Game-Based Learning Using Brain Measures 165
Fig. 10.1 Expected progress of attention level based on different methods for triggering simulator
events; timed interval (frequent as well as infrequent intervals) and BCI controlled (set to target
attention optimal values)
10.2 Method
To determine if BCI devices can be used to more effectively trigger scenario events,
a pilot experiment is set up. The between subjects design compares two condi-
tions—time interval triggered vs. mental state triggered simulator events.
Participants are asked to perform a short training by flying a helicopter around an
urban area in a low fidelity (gaming) simulated environment. The objective of the
training is to familiarize with basic helicopter control mechanisms (pitch, role, and
yaw). The training task consists of flying through consecutive augmented cues, a
kind of “virtual checkpoints” in the sky. These hoop-shaped checkpoints are placed
in a track configuration and are located on different heights.
Eight participants (7 male, 1 female; age ranges from 21 to 36 years with an aver-
age of 27) are randomly assigned to either on one condition. The test starts with a
1 min familiarization of the task, where the task is explained. Once the participants
understand the task, the helicopter control training commences.
The training task is identical for all participants: to learn to control the helicopter
by flying through a set of consecutive digital “checkpoints.” This training takes 5 min
to complete. Depending on the assigned condition, the task either automatically
increases in difficulty (time-based interval condition, “A”) or varies in difficulty
depending on the participant’s attention level (mental state-based condition, “B”).
In the time-based interval condition (A), to increase difficulty the checkpoint
diameter decreases gradually over 5 min. This reduction triggers regardless of how
well the trainee performs. In the mental state-based (B) condition, the task complex-
ity changes on the basis of the level of attention of the trainee. When strained by the
task, trainee attention will increase, thus increasing the diameter of the checkpoints.
When the task no longer requires high attention (through increased mastery of the
controls), the checkpoint diameter will remain constant. When the attention level
becomes too low, the checkpoint diameter will dynamically decrease, thus increas-
ing the task complexity. During training, the checkpoint diameter decreases when
166 J. van der Pal et al.
the participant’s attention level is higher than 70 and increases when attention level
is lower than 30 on a scale from 0 to 100. An optimal level of attention is achieved
between 30 and 70. The checkpoint diameter does not change between these levels.
The total checkpoint diameter size reduction over 5 min in condition B is therefore
not known beforehand and depends on the participants’ efficiency in mastering the
task.
After completing the helicopter control training, all participants receive the same
test, where they are required to fly one track with the smallest checkpoints used dur-
ing the training. Trainee performance is determined by the number of checkpoints
correctly flown through and the time needed to finish the track. A post-experiment
questionnaire measured subjective ratings on the amount of challenge experienced.
10.2.1 Apparatus
10.2.1.1 BCI Tooling
Neurosky Mindwave Mobile (see Fig. 10.2 for a drawing of its components) is a
single channel EEG device with a dry sensor positioned on the forehead (approxi-
mately Fp1 position). The real-time processed measure used for BCI in this study is
attention. Neurosky does not reveal the exact composition of this measure, but indi-
cates that the attention is based primarily on beta waves. Attention is personally
calibrated scale from 1–100, with interpretations: 1–20 strongly lowered, 20–40
reduced, 40–60 neutral, 60–80 slightly elevated, and 80–100 elevated.
10.2.1.2 Helicopter Control Training Game
The Helicopter Control Training Game (see Fig. 10.3) is a low fidelity simulation
environment developed using the Unity engine in the XLab at the Netherlands
Aerospace Centre—NLR. The game is used to familiarize participants with basic
principles of helicopter controls such as pitch, roll, and yaw. The simulation features
highly simplified helicopter flight models and controls, allowing for relatively easy
mastery of basic flight control. The task is to fly through “augmented hoops” in the
sky. The hoops change from large to small in the time-based condition, while in the
mental state-based condition the hoops vary as a function of attention level.
10.3 R esults
All participants completed the experiment successfully. Unexpectedly, participants
in the BCI controlled condition did not perform better on the test than participants
in the time interval controlled condition (see Table 10.2 for results).
10 Exploring Adaptive Game-Based Learning Using Brain Measures 167
Fig. 10.2 Mindwave
Mobile
Participants in the time-based condition spent an average of 26 s more outside of
the optimal attention range (25% of total training time) compared to participants in
the BCI controlled condition (16% of total training time), t = 2.22, p < 0.05 (com-
paring the percentages time deviating from the optimal band). For some partici-
pants, the attention level graphs showed clearly that whenever a participant’s
attention level surpassed the threshold, the task difficulty would change, causing the
participant’s attention level to normalize in turn. For other participants, BCI triggers
are less clearly or not always associated to excess of the optimal attention range. For
example, Fig. 10.4 illustrates five correct BCI triggers, but the triggers at 135 and
210 s seem to be influenced by EEG spikes and lead to incorrect events. Later
attention levels (at 250, 280 and 290 s) should have been detected and events should
have been triggered. One participant (mental state-based condition) remained in the
optimal attention range, but kept performing poorly and ended up with zero correct
checkpoints in the test.
Another participant with an apparent preexisting high skill level (and the maxi-
mum score on the test) lost interest after 110 s but gained attention to an optimal
level after the task difficulty was boosted several times (see Fig. 10.5).
Participants in the time-based condition varied considerably in overall attention
level (either very high or very low), but did not differ much in test scores. For two
participants in the time-based condition the subjective ratings were inconsistent to
the measured attention levels: intermediate challenging (5) versus high attention
levels, and rather challenging (8) versus low attention levels.
168 J. van der Pal et al.
Fig. 10.3 Screenshot of Helicopter Control Training Game
Table 10.2 Means and standard deviations (in brackets) of the results on the Helicopter Control
Training Game for the conditions time-based and mental state-based control of task difficulty
Condition Total sum deviation Percentage of time deviating Test Experienced
from optimal attention from optimal attention range score challenge
A time based range during training during training 4.0 (0.7) 7.3 (1.3)
(n = 4) 839 (323) 24.9 (51.8)
2.0 (1.9) 8.0 (1.4)
B mental state 611 (255) 16.2 (10.0)
based (n = 4)
Test score indicates the average number of correctly flown checkpoints; experienced challenge
indicates the average subjective rating from 1 to 10 (1 = easy, 10 = hard)
10.4 Discussion
This exploratory study was set up to determine whether BCI devices can be used to
more effectively trigger scenario events in realistic training settings. The study
revealed the potential of BCI for training as well as some improvements to make.
BCI using the Mindwave attention level functions reasonably well to adjust the
task difficulty by increasing or decreasing the diameter of an augmented hoop in
the sky. The adaptive training group remained longer in the optimal attention band
than the time-based training group. However, this did not lead to better test scores.
The allotted training time of 5 min may not have been sufficient to increase the
performance of participants with poor initial skill level. For fair comparison of the
conditions, the time intervals should be based on the average learning curve of the
intended training audience. The small groups should have been controlled for pre-
existing skill levels (e.g., game experience in general and experience with flight
simulators in particular).
10 Exploring Adaptive Game-Based Learning Using Brain Measures 169
Fig. 10.4 Attention level (direct measures in blue line, weighted average in red line) and event
triggers (circles) for participant 1
Fig. 10.5 Attention level (direct measures in blue line, weighted average in red line) and event
triggers (circles) for participant 3
The events triggered may also require further development. How much easier or
harder should the task get with every step? Would a just noticeable difference lead
to a smooth and optimal change in task difficulty or is a more considerable change
required to ensure motivation and engagement is maintained?
Other potential improvements relate to technical adjustments in the attention
level criteria to increase reliability of the results. For example, some events were
triggered by EEG spikes that are not brain wave related but caused by muscle move-
ments or electromagnetic disturbances. An improved algorithm to detect and filter
such artefacts is needed and/or the algorithm for trigger events should be adjusted
to become insensitive to EEG spikes. Furthermore, the current optimal band (30–
70) for attention was set arbitrary. More insight is needed in the criteria for the
thresholds to trigger events.
170 J. van der Pal et al.
The Mindwave attention level may be used as a rough motivational indicator the
trainees have to the task, but other EEG indicators may be more clearly linked to
task difficulty (increased theta band) or cognitive load (reduced high alpha band).
Using these measures for BCI purposes will require some additional real-time algo-
rithms to be developed as well as further studies to increase the knowledge base on
the reliability and validity of the mental state indices for cognitive load, including
but not limiting to EEG-based measures.
Other wearable and wireless cognitive load indicators (heart rate, pupil diameter,
etc.) are already available and practical daily use may be achievable when they are
integrated to smartwatches, smartglasses, and headsets. Combining the cognitive
load measures and using software for automatic calibration and personalized classi-
fiers for the optimal personal selection of measures is no longer science fiction for a
distance future.
Despite the high readiness level of technological elements for adaptive training,
further research is in demand to generate the knowledge base on the conditions and
criteria for reliable and valid measures and their usage for adaptive training. This
should include systematic study of the combination of physiological, behavioral,
and performance measures to an integral model for adaptive training.
Application for such an integral model for automated adaptive training may be
limited to relatively simple part-task training. In educational and professional train-
ing settings, this may already be a tremendous gain in terms of training time, student
motivation, and reducing training costs. Full application to whole-task training may
not be feasible, partly because in many professional training domains, whole-task
training implies team training, and partly because the training scenarios may be too
complex for automated real-time control. Even when this would be technically
achievable, the analysis and programming for it may be too laborious and costly.
For those cases, the BCI supported adaptive training model may provide input to the
instructor, and leave the real-time adjustments to his or her insight.
In sum, BCI controlled training using EEG devices that are easy to apply in real
training settings appears to be viable, although considerable effort is needed to ensure
the measurements and the trigger events are well-tuned to the training audience char-
acteristics such as the learning curve. Based on the potential demonstrated in the cur-
rent exploratory study, further development and experimentation is planned for.
Acknowledgments This study was partly funded by the Royal Netherlands Air Force under
contract 080.14.3903.10 (Serious Gaming program). We would to thank Leon Berghorst and
Christian Rosheuvel for developing the Helicopter Control Training game and their support during
running the experiment.
10 Exploring Adaptive Game-Based Learning Using Brain Measures 171
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Chapter 11
Academic Retention in the Italian Context
Maria Lidia Mascia, Mirian Agus, Gianrico Dettori, Maria Assunta Zanetti,
Eliano Pessa, and Maria Pietronilla Penna
Abstract This study analyzes if motivation, academic self-concept, perception of
the time perspective, self-regulation, and the attendance of specific online labora-
tory activities influence academic retention and achievement of two group of fresh-
men attending the first year of their Bachelor’s Degree. The freshmen were
monitored along their first academic year. In particular, we try to understand which
factors can help student to overcome the transition gap created by the passage from
high school to university. The choice of the implementation of an online lab is due
to evidence that online platforms are tools that can help to reduce the academic
dropout. These platforms allowed students to use a supporting network, but, at the
same time, students can autonomously take advantage of suitable materials to
achieve their learning goals and to bridge an orientation gap. In Italy, this gap is
often present in the transition between high school and university. In general, we
can say that the experience of the online laboratory was positive and combined with
the enhancement of motivation, academic self-concept, vision of the time perspec-
tive, and self-regulation can represent an important support above all for the Italian
freshmen.
11.1 Introduction
Academic success, especially in the Italian context, is strongly linked to overcom-
ing the first year of academic studies. In fact, very often, the phenomenon of univer-
sity dropout and academic dispersion coincides greatly with the failure to adapt to
the new school context [1–3].
M. L. Mascia (*) · M. Agus · G. Dettori · M. P. Penna 173
Department of Pedagogy, Psychology, Philosophy, University of Cagliari, Cagliari, Italy
e-mail: [email protected]; [email protected];
[email protected]; [email protected]
M. A. Zanetti · E. Pessa
Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
e-mail: [email protected]; [email protected]
© Springer International Publishing AG 2018
D. Sampson et al. (eds.), Digital Technologies: Sustainable Innovations for
Improving Teaching and Learning, https://doi.org/10.1007/978-3-319-73417-0_11
174 M. L. Mascia et al.
In particular, in the Italian context, University dropout is a real problem of the
higher education system with a significant wastefulness of resources, mainly
publicly funded [1, 4, 5]. In Italy, the transition from high school to University is
particularly deficient with respect to the international context [6].
At the moment, models seeking to explain the variables involved in the transition
process and maintaining the choice are primarily of the US or Anglo-Saxon matrix,
which makes it not easily translatable and applicable to the Italian context. In par-
ticular, the national context shows specific features that make it almost completely
unmatched to international education systems [7].
These data are found both in Europe and in Italian context, showing the presence
of forms of school failure, often linked to strong dropout rates or serious delays in
obtaining the title over institutional times. These data, as well as contextual vari-
ables, are often attributable to factors related to the inadequacy of study strategies
or even inadequate procedures for greater autonomy required by the university sys-
tem in learning management. This element is also confirmed by a comparison
between the American and European systems. In fact, the first system provides the
student with more feedback and support so that the gap between secondary school
and university is not too traumatic [8].
Another weakness point in the national academic system is the lack of support
that leads the student to have a less traumatic insertion into the new educational
reality. Although orientation paths for newbies have increased in recent years, there
are still no supports to get closer to the student’s reality and provide him with orga-
nizational, didactic, and motivational support.
Taking into account these elements and the literature statements, the principal
aim of this research is to explore specific psychological and individual dimensions
related to academic success or failure. By means of a mixed model, for groups of
longitudinal nature and for others of a transversal nature, we investigated a variety
of dimensions related to academic success/failure and proposed the activation of an
online laboratory to enhance these dimensions. The study examines the career and
the path of students enrolled, respectively, in the first year of the Degree in Education
and Training at the University of Cagliari. Among the variables present in the litera-
ture, we take into account the role of motivation, self-concept, perception of the
time perspective, and self-regulation [9–11] in order to research some models that
can give support to academic retention.
11.1.1 Main Psychological Factors: Theoretical Frame
What are the psychological dimensions we will consider?
Motivation: many searches identify in the motivation [12, 13] of the student one
of the key predictor factors of success.
Motivation is widely studied in the educational context of influencing the transi-
tion from one cycle to another in determining the student’s performance during the
academic course [13]. Motivation behavior is a recurring scheme that combines
11 Academic Retention in the Italian Context 175
various cognitive and affective factors that determine the beginning and mainte-
nance of a goal. In this sense, school motivation is seen as a dynamic process.
Academic motivation can be defined as the whole of the reasons that bring the
student to act in order to reach different objectives. Among the many motivational
theories, the one that raises a fundamental interest in education is the Self-
Determination Theory [14], according to which an active subject, projected towards
personal growth, has three fundamental motivational needs: to feel independent, to
feel competent, and to relate to others [15]. The motivation would be much more
favored by contexts that allow these needs to be met. At the basis of self-determined
conduct, there is the need to feel artisans of their actions and to freely choose the
task and its mode of conduct.
The use of incentives can trigger an extrinsic motivation that can give good
results when people have to perform tasks that do not attract them, but extrinsic
motivation is not lasting and needs to identify intrinsic motivational support. In the
Self Determination Theory [12, 16], the student is an active body that tends to real-
ize its capabilities and develop the various aspects of its personality in interaction
with the environment. The type of motivation of the individual (amotivation, extrin-
sic motivation, intrinsic motivation) is closely related both to the nature of behavior
(determined externally or self-determined) and to the type of internal regulation. All
this is done through a process of interiorization or the tendency to transform the
external regulation of behaviors into internal regulation, allowing the implementa-
tion of self-determined behaviors and intrinsic motivation [12]. The transition from
external to internal regulation is progressive, depends on the satisfaction of the need
for relationships, explains the way in which interests, values, and goals become part
of the Self. Studies show that self-determined motivation leads to less academic
abandonment [17, 18], less absenteeism and better results in studies [19, 20], persis-
tent frequencies, and participation in lessons to activate effective strategies for cop-
ing with academic difficulties.
Academic Self-concept: it includes attitudes, feelings, and perceptions about own
scholastic and intellectual abilities.
There are three great theoretical models relating to the causal order of the above-
mentioned relationship: the self-enhancement model [21], the skill development
model [21], and the reciprocal effects model [22]. For the self-enhancement model,
high academic concepts exclude negative thoughts and failure, thus improving their
school performance [23].
For the reciprocal effects model, skill improvement leads to more satisfying
results, which in turn favor their own perception of self. Starting from these assump-
tions, Marsh elaborates a model similar to the previous ones, gaining more confir-
mations than the above. According to the Marsh model, it is not the concept of
self-affecting academic results or vice versa, but there is a continuous interaction
and influence between the two variables. Consequently, a positive change in their
own academic concept would lead to better results, and better results would increase
their own perception of self [24].
Longitudinal studies show that the academic self-concept is closely linked to
long-term academic aspirations such as academic choices and academic outcomes
176 M. L. Mascia et al.
[24–26]. Empirical evidence, therefore, seems to confirm that the academic
self-c oncept and school outcomes affect each other, showing a significant relation-
ship between each other.
Temporal perspective: many studies aimed primarily at teenagers have shown
that those who are more oriented to the present and the negative past are less able to
plan a realistic goal path [27]. They use less self-regulation strategies and report a
higher level of anxiety. Those with a higher level of orientation to the future and the
positive past show greater skills in establishing and achieving goals, knowing how
to self-regulate their behavior and show high levels of willingness and ability to plan
strategies to meet long-term goals. For males, school success is best predicted by
the disorientation dimension while for females is a low level of anxiety. The orienta-
tion dimension to the future and the self-regulation of learning are linked to each
other and represent significant predictors of school success [28]. This orientation is,
however, confirmed by studies conducted on samples of university students [29].
Self-regulation: numerous studies confirm the positive influence of a good level
of self-regulation on academic success [30–32], that is, literature shows that heavily
self-determined students know what they want to learn, plan, and control their
learning process using the strategies best suited for this purpose, control the results
obtained, and, if necessary, redefine cycles or change their goals in function of what
they have experienced [33–35].
In addition, the literature outlines a positive correlation between academic per-
formance and self-regulation strategies [36–38], learning self-regulation [39], and
metacognition [33, 40–43]. Self-regulatory competence, coupled with a metacogni-
tive reflection on one’s own work, is increasingly being referred to as a crucial ele-
ment to consider in the structuring of an educational and training path. The concept
of self-regulation has now become central to research that deals with the process of
studying, learning, and creating an individual’s educational and professional cur-
riculum [44].
However, acquisition of self-regulatory competence does not always develop
automatically and spontaneously [45] but requires continuous practice and the exis-
tence of a supportive and reflective environment. Based on these considerations, it
has been hypothesized that an environment capable of supporting the acquisition of
self-regulatory and metacognitive skills may be that of a web-based type [46, 47].
This support has a dual potential: it can promote the development of self-regulation
during the academic course and can support the student in the transition between
high school and the academic world through initial support and monitoring over
time.
11.1.2 Online Learning Environments
Starting from this and observing what is proposed by international academic sce-
narios, we have noticed how online learning environments are gaining in value by
providing tools that can be more easily supported by the academic path in its
11 Academic Retention in the Italian Context 177
becoming. The literature shows how scaffolding and support are fundamental, and
technological tools can be considered as an important support to self-regulation and
metacognitive reflection [48]. Thanks to these, it is possible to better trace the work
done by the student and also give him an account of what he has accomplished and
the progress he has made.
There are now a number of researches that argue that appropriate support appears
to be a protective factor against academic dispersion [49].
It is from these considerations that the idea is to initiate online courses aimed at
students enrolled in the first year of the Bachelor of Science in Education on the
Moodle platform of the University of Cagliari. Based on the intervention in place, a
series of analysis and considerations were made to verify the effectiveness of the
tool and the proposed activities in terms of enhancing self-regulation and achieving
academic success, avoiding the risk of falling-out. In recent years, information and
communication technologies have had a profound impact on training methods and
knowledge management. In addition, the globalization of markets and global inter-
actions has meant that individuals are continually subjected to an upgrade of their
skills and abilities through a process of lifelong learning, that is, continuous learn-
ing. Literature shows how significant the relationship between technology and self-
regulation appears to be. In particular, it emerges that the use of education-based
technologies entails the freedom to manage the learning process in comparison to
traditional school environments and allows the start of a self-regulation process
[50]. In literature, it also emerges that promoting development and maintaining self-
regulation is favored by the creation and structuring of favorable environments that
allow us to control the essential dimensions of learning and provide opportunities
for reflection and review [51]. Moreover, through the creation of online support
environments, the conceptual, meta-cognitive, procedural, and strategic learning aid
is promoted in learning [52, 53]. There are many researches that demonstrate the
utility of the computer in the educational field, just because of the ability to get
beyond the presence on multiple stages of learning and thus help the student to
acquire metacognitive skills that can then be translated into multiple learning envi-
ronments [54].
The challenge of education for a global society focuses on developing and iden-
tifying skills that can be acquired autonomously through technological tools.
Particularly interesting are the learning environments, seen as connection systems
between teaching design and teaching, formal and nonformal learning, teachers and
students.
A lack of self-regulation, coupled with a lack of motivation, can lead to regular
moments of apathy and disinterest in the study [55]. Especially students with diffi-
culties must have not only a knowledge but also a metacognition of why they should
use certain tools. To support the student at times of difficulty, the traditional aca-
demic context should offer him, through teachers and tutors, a series of activities,
easily reproducible and reusable autonomously, aiming at overcoming the difficult
time. In order to promote greater self-regulation, more group activities should be
proposed, more feedback should be provided for self-assessment, identification of
learning styles, and time management. In general, behavior in learning environ-
178 M. L. Mascia et al.
ments involves: creating appropriate learning objectives and implementing an action
plan for a learning session, implementing effective learning strategies, increasing
self-understanding, and monitoring the adequacy of content throughout the learning
session.
The multimedia learning environment brings with it a series of potential for full
student training, in terms of learning, in terms of socialization, and in terms of
reflection and feedback [56]. Literature indicates how technologies can encourage
the initiation of a self-regulation process [50, 53] and vice versa, as students adopt-
ing more effective learning strategies tend to achieve higher levels of learning, espe-
cially in environments of multimedia learning [57].
11.2 M ethod
11.2.1 H ypothesis
The main objective was to analyze the specific and combined effects of social-
demographic variables, motivation, academic self-concept, self-regulation, future
perspective, online support as factors of academic retention. In this regard, self-
regulation is closely related to the motivation [58]. There are also numerous studies
that associate self-regulation with the future temporal perspective [10, 59]. In light
of these considerations on two groups of students (Group A and Group B), some
models of path analysis have been created in order to assess the presence of the
causal relationships between psychological variables and academic success and
above all in order to verify the predictive capacity in a longitudinal direction.
Hypothesis 1: is it present a positive correlation between academic self-concept,
motivation, self-regulation, time perspective, frequency at the online laboratory, and
the academic success?
Hypothesis 2: is it possible to create a retention model based on the association
among the observed variables?
11.2.2 Materials
Students were presented a standardized multidimensional protocol, constituted by:
• Academic motivation scale [60]: Academic motivation scale is one of the most
used scales to measure academic motivation. Structured in the perspective of the
theory of self-determination [16], it has been integrated into an empirical model
that includes both factors of the motivational determinants (school environment,
engagement, and teacher behavior) and possible consequences (dropout, school
performance, interest), providing support for constructive and predictive validity
[18]. The original version of the scale [17] is characterized by 28 items and
11 Academic Retention in the Italian Context 179
evaluates seven forms of motivation, three of which measure different types of
intrinsic motivation and four evaluate different types of extrinsic motivation. The
questionnaire uses how to measure a Likert scale at ten steps from 1, “nothing at
all,” to 10, “totally true.” The five subscales reflect amotivation, extrinsic extrin-
sic motivation, intrinsic extrinsic motivation, identified extrinsic motivation, and
intrinsic motivation. In this chapter, the Italian validation of the scale carried out
by Alivernini and Lucidi in [60] confirms the factorial structure with five factors
with values of χ2/df = 3.30, TLI = 0.93, IFI = 0.94, CFI = 0.94, and RMSEA = 0.06
as well as a good internal consistency (from 0.81 to 0.87).
• Self-description questionnaire III [61]: Self-description questionnaire III is a
self-assessment tool based on the hierarchical and multiformity model of
Shavelson et al. [62] and consists of a self-assessment form consisting of 13
scales: 4 relating to the concept of academic self and 8 relating to the concept of
nonacademic self. In this study, the scale used consists of 3 subscales (general
academic self-concept, verbal self, and mathematical self) each of which con-
sists of 10 items. The results of the analyses confirmed the three-factor structure
by explaining overall 62.4% of the total variance with good data adaptation
(RMSEA CFI, NNFI, and RMSR (RMSEA = 0.07; CFI = 0.92; NNFI = 0.90;
RMSR = 0.06) and a good internal consistency (0.88).
• Self-regulation questionnaire [63]: The questionnaire on self-regulation aims
to identify the components of the self-regulated approach to the study, with
particular reference to three aspects of the metacognitive type: skills of pro-
cessing, organization, and self-evaluation. Research show how best students
can organize their study activity with a time-bound work program complying
with commitments and deadlines [63, 64] using schema-driven strategies
(based on schematization, building diagrams and tables, notepadging, etc.) and
a greater number of previous knowledge, adopting deep processing methods by
properly selecting the main aspects. Finally, the successful student is aware of
his own method of study, knows how to properly assess his/her own prepara-
tion, and tends to reflect more often on the best way to deal with the study. The
scale consists of 30 items, 10 for each of the aspects considered to be answered
by a Likert scale from 1 to 5.
• Stanford Time Perspective Inventory—short form [59]: The tool used is an
Italian validation of the Stanford Time Perspective Inventory (STPI). The origi-
nal version of the tool is made up of 56 items, while the English version of the
short form is made up of 22 items to which subjects must respond using a Likert
scale from 1 to 5. The factorial analysis was performed on a sample of 1507
subjects (965 women and 542 men). The results showed a clear correspondence
between the factor components and the a priori hypothesized dimensions (future,
fatalistic, and hedonic presentations). The Italian validation of the instrument
shows good internal consistency.
• Furthermore, the students were presented the Online laboratory: the online labo-
ratory was supporting the General Psychology class. The proposed activities
supported students in their studies and provided guidance during their first aca-
demic year. The laboratory provides for the presence of the online tutor as man-
180 M. L. Mascia et al.
ager and supervisor of contents and interactions [65–67]. The class was entirely
available on the University Moodle platform (moodle.unica.it) [68]. The activi-
ties were those allowed by the platform, namely, forum, documents, chat, and
learning objects on topics already covered. The design phases of Azevedo and
Hadwin [52] were followed by the planning of the activities of the laboratory, in
particular a design aimed at encouraging motivation and self-regulation pro-
cesses [69]. The model mainly referred to as the structure of the course was the
one reproduced in the Italian version of Giannetti [53] which provides for the
organization of the contents of some key elements. Support for orientation within
the environment is provided by the use of the platform that is usable and acces-
sible, and it also contains a number of elements that favor the navigation espe-
cially thanks to the intuition of the graphic interface [53].
Job planning support was given with the proposal of a task calendar and the clar-
ity of the presentation of the resources. Support for task execution and activity mon-
itoring has been provided with useful materials both in text format and in digital
format. At this stage, the use of discussion forums, sometimes initiated by the tutor
and several times by the students themselves, has become very important. The feed-
back was constant and numerous; they involved both didactic and organizational
aspects and self-regulation. Finally, there has been a continuous support for the
work done, a constant presence of support in the study and reflection on its own path
aimed at detecting the main mistakes committed and redefining them.
The tutor also answered orientation questions. In this study, academic success
was measured through the number of credits reached by the student at the end of the
first academic year.
11.2.3 P rocedure
The protocol was given in a single administration, in paper format, and the distribu-
tion has been collective. Students were tested in one session during the first semes-
ter and were divided in two groups: participants only to frontal lectures and
participants both to frontal lectures and to Moodle online laboratory.
11.2.4 Participants
The research work is aimed at two groups of students enrolled in the first year of the
Degree Course in Education Sciences, that is the observation of freshmen for 2
years. In particular, models of academic success have been developed which have
integrated a number of aspects, in order to study a relation among variables in a
longitudinal way.
11 Academic Retention in the Italian Context 181
The observed variables were represented by the values of nine indicators: Lab
(attendance to the laboratory), Academic Success (number of obtained credits),
Academic Self (assessment of academic self-concept), Amotivation (degree of amo-
tivation in Academic Motivation Scale), Intrinsic Motivation (degree of intrinsic
motivation in Academic Motivation Scale), Self-regulation (assessment of self-
regulation ability), Hedonistic Present (assessment of tendency towards Hedonistic
Present attitude in Stanford Time Perspective Inventory), Fatalistic Present (assess-
ment of tendency towards Fatalistic Present attitude in Stanford Time Perspective
Inventory), and Future (assessment of tendency towards Future attitude in Stanford
Time Perspective Inventory).
11.3 G roup A: Participants and Results
A sample of students (131) enrolled in the academic year 2010/2011 at the degree
program in Education and Training Sciences took part in the first pilot study. The
sample consists of 123 females and 8 males aged 18 to 28 (M = 24.25, SD = 3.77).
The students came from several higher education institutions, with a percentage of
44% from high schools, 18.9% from technical institutes, and 37.1% from high
schools. 18.3% came from the municipality belonging to the study course, 51.1%
from the province, and 30.5% from outside the province of Cagliari. As for socio-
economic status, the sample was divided as follows: 28.1% had a low level, 28.8%
average, and 42.4% high.
The correlation gives a positive association between high levels of self-r egulation
and future perspective characterized by a present behavior dominated by an effort to
reach future goals and rewards, self-determined motivation, and academic success.
A negative association emerges between levels of self-regulation and a fatalistic
present, that is, a far-sighted and “here and now” attitude. There is still a positive
correlation between intrinsic motivation and the concept of academic self, self-
regulation, and academic concept between itself and success, as well as positive
associations between the perception of a future characterized by a present behavior
dominated by the effort to achieve their goals and academic success. Finally, there
are negative correlations between levels of “amotivation” and the concept of self
and academic success (Table 11.1).
The values present in the correlation table and the use of a path analysis allow
proposing a retention model.
The model (see Fig. 11.1) presents a causal relationship between high self-
concept and self-determined motivation. The high self-concept positively influences
the ability to self-regulate in studies which, in turn, influence both the success in the
studies and the future temporal perspective of the student. Finally, the future tempo-
ral perspective has a positive impact on academic success.
Table 11.1 Correlation among variables observed (*p < 0.05—**p < 0.01) 182 M. L. Mascia et al.
12 34 5 6 7 8 9
1 Lab 1 1 1 1 1 1
−0.214* 0.235** 0.047 0.461** −0.322**
2 Academic success 0.163 1 0.085 −0.023 −0.187* −0.383**
0.087 0.007 0.243**
3 Self-esteem 0.012 −0.024 1 1 −0.033 0.074
4 Academic self 0.191* 0.196* 0.186* −0.173*
5 Amotivation −0.036 −0.225** −0.103 −0.510**
6 Intrinsic motivation 0.014 0.199* 0.167 0.432**
7 Autoregulation 0.091 0.234** −0.09 0.002
8 Hedonistic present 0.178* −0.048
9 Fatalistic present 0.001 −0.099 0.075 −0.071
10 Futur 0.064 −0.033 −0.007
0.343** −0.007 0.124
11 Academic Retention in the Italian Context 183
Fig. 11.1 Path analysis model: Group A
11.4 Group B: Participants and Results
The group B is composed by 174 females and 11 males aged between 19 and
54 years (M = 24.37; SD = 6.32). Students came from different high schools, with
percentages of 37.3% from high schools, 18.9% from technical schools, and 43.2%
from secondary school diploma with specialization in teacher training. In the cur-
rent study, 28% were from the municipality (Cagliari), 52% from the province, and
20% from outside the province of Cagliari. Finally, 71% of the sample had a low
average high school diploma grade, while 29% of the sample had a high average
high school diploma grade.
In this group, the analysis of the matrix of correlations between variables shown
below (see Table 11.2) evidences the emergence of positive associations between
high levels of self-regulation and future time perspective. This characterizes a
behavior dominated by an effort to achieve goals and future rewards, participation
in the laboratory, intrinsic motivation, academic success, and good academic self-
concept. We also observe a positive correlation between intrinsic motivation, aca-
demic self-concept, and participation to the online laboratory activities, as well as
with self-regulation and self-concept, and between the latter and the academic suc-
cess. The data highlight the negative correlations between levels of “amotivation”
and intrinsic motivation and future time perspective.
The values present in the correlation table and the use of a path analysis allow
proposing a retention model, whose logical structure is depicted below (see
Fig. 11.2).
The model shows a causal relationship between high self-concept and self-
determined motivation. The high self-concept influences positively the ability to
self-regulate in studies, which, in turn, affects both the success in studies and future
Table 11.2 Correlation among variables observed (*p < 0.05—**p < 0.01) 184 M. L. Mascia et al.
1 234 5 6 7 8 9
1 Lab 1 1 1 1 1 1
−0.461** 0.389** −0.089 0.679** −0.195**
2 Academic success 0.199** 1 −0.165* −0.124 −0.083 −0.213**
0.063 −0.079 0.438**
3 Self esteem 0.082 0.090 1 0.022 0.706**
−0.116
4 Academic self 0.002 0.075 0.373** 1
5 Amotivation 0.048 −0.045 0.059 −0.027
0.140 0.057 0.220**
6 Intrinsic motivation 0.052
7 Autoregulation 0.105 0.216** 0.035 0.075
8 Hedonistic present 0.125 −0.058 0.068 −0.060
9 Fatalistic present −0.072 0.009 −0.056
10 Future −0.227** 0.164* 0.128 0.188*
−0.036
11 Academic Retention in the Italian Context 185
Fig. 11.2 Path analysis model: Group B
time perspective of the student. The latter finally has a positive weight on academic
achievement. This path analysis is interesting because it shows the influences among
variables in order to create an interrelation system and a set of tools in order to sup-
port academic success and retention.
11.5 Conclusion
Dropout is based on the probability of students not continuing a specific academic
programe [70], so it is fundamental to understand what types of variables could
have a key role in succeeding in leading the student to success, avoiding dropouts,
and maintaining a persistent profit within the system.
In the Italian context, students find the transition to higher education stressful, so
it becomes difficult to be a student in a learning environment that requires higher
levels of autonomy, initiative, and self-regulation than the high school system.
Students’ inability to cope this transitional period contributes to poor academic per-
formance and to increase dropout rates among freshmen [3].
In this study, motivation, academic self, self-regulation, and future perspective,
related to the achievement of academic success, have been measured. Results con-
firm the relationships found in the literature among the variables investigated, with
particular emphasis on the role played by self-regulation in academic success.
Correlation analysis and path analysis were applied in order to identify the differ-
ences among dimensions. The main results confirm the evaluations carried out dur-
ing the previous years of experience of the online laboratory, highlighting the
benefits perceived by the involved students, useful in promoting an approach
towards motivation, self-regulation, and academic achievement.
186 M. L. Mascia et al.
Such a study may be the basis for subsequent studies, as there are not many lon-
gitudinal studies in the Italian context that investigate the causes of dropout and
possible systemic solutions.
Tools such as the online lab may prove to be fundamental as they represent a
continuum between high school and university, and the presence of online labs leads
to the consolidation of some variables that play a central role in the retention pro-
cess. In particular, they allow the student to create a network of contacts that can
support it beyond frontal lessons; the proposed activities stimulate intrinsic motiva-
tion, academic self, and self-regulation in view of a future perspective. Data on the
laboratory attendance show that students had benefits in terms of strengthening and
growth of all variables considered as crucial for the development of academic suc-
cess and retention in the university system. Today, both through the activity of the
online laboratory and its data collection work (in more University courses), we are
allowed to pursue the goal of finding a model that can support the academic success
in the Italian context and can be the base to create a solid bridge between high
school and University. As literature underlines, the ability to predict dropouts and
improve retention is a still complex issue that involves the number of intercorrelated
and distinct factors. It is important to study these factors in different context in order
to understand which strategies can be more effective [71] and to be aware of use of
online tools to improve academic offer and quality.
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Demetrios Sampson,Dirk Ifenthaler,J. Michael Spector,Pedro Isaías (eds.) - Digital Technologies_ Sustainable Innovations for Improving Teaching and Learning-Springer International Publishing (201
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