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ORIGINAL RESEARCH
published: 01 June 2022
doi: 10.3389/feduc.2022.789397
Edited by:
Christos Troussas,
University of West Attica, Greece
Reviewed by:
Andreas Marougkas,
University of West Attica, Greece
Anna Mavroudi,
Norwegian University of Science
and Technology Museum, Norway
*Correspondence:
Charoula Angeli
cangeli@ucy.ac.cy
Specialty section:
This article was submitted to
Digital Education,
a section of the journal
Frontiers in Education
Received: 04 October 2021
Accepted: 25 April 2022
Published: 01 June 2022
Citation:
Christodoulou A and Angeli C
(2022) Adaptive Learning Techniques
for a Personalized Educational
Software in Developing Teachers’
Technological Pedagogical Content
Knowledge. Front. Educ. 7:789397.
doi: 10.3389/feduc.2022.789397
Adaptive Learning Techniques for a
Personalized Educational Software in
Developing Teachers’ Technological
Pedagogical Content Knowledge
Andri Christodoulou and Charoula Angeli*
Department of Education, University of Cyprus, Nicosia, Cyprus
The authors discuss the design and utilization of e-TPCK, a self-paced adaptive
electronic learning environment designed to promote the development of teachers’
Technological Pedagogical Content Knowledge (TPCK). The system employs a
technological solution that promotes teachers’ ongoing TPCK development by engaging
them in personalized learning experiences using technology-infused design scenarios.
Results from an experimental research study revealed that the experimental group
outperformed the control group in developing TPCK on two design tasks, substantiating
the claim that the use of technology can better facilitate the development of TPCK
than traditional instruction. Furthermore, e-TPCK’s adaptive guidance using worked-out
examples led to mathemagenic learning effects for novice student-teachers, while for
the more experienced student-teachers learning with e-TPCK proved beneficial only at
the beginning of their engagement with the system. Hence, the relative effectiveness
of instructional techniques embedded in e-TPCK became less effective as a result of
student-teachers’ increasing expertise, providing evidence for the expertise reversal
effect. In conclusion, the findings of the research are encouraging for the advantages
of personalized learning in teacher education and showed that the design of adaptive
learning technologies must account for learners’ prior knowledge and changing levels
of expertise throughout the ongoing interaction with the computer system.
Keywords: adaptive learning, personalized learning, higher education, student-teachers, TPCK development
INTRODUCTION
In recent decades, teaching approaches in education have shifted from teacher-centered to
learner-centered methods (Cuban, 1993;Barr and Tagg, 1995;Watson et al., 2012;Alamri et al.,
2020). The acceptance of the constructivist view of learning led to the advocacy of the learner-
centered approach in education as a more effective and successful instructional method in which
instructional approaches and content are focused on the individual learner. Researchers and
practitioners have praised the potential of the learner-centered instructional paradigm to address
diverse learner needs and improve learner engagement and achievement. This paradigm shift led
to a change in the roles of teachers and learners; teachers became designers and facilitators of
learning rather than controllers of the learning process, while learners became not only active
collaborators and participants in the construction of knowledge, but also autonomously responsible
for their own learning (Barr and Tagg, 1995). However, the predominant teaching model in higher
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Christodoulou and Angeli Adaptive Learning in Teacher Education
education can still be described as one that promotes the teacher
as the center on the stage; someone who stands in front and
teaches the content to the people sitting in rows one after
the other. Instructors in higher education often rely on the
“one-size-fits-all” model to deliver a standardized curriculum,
assuming that all learners have similar characteristics without
addressing their individual differences (Demski, 2012). Not every
student is the same, and higher education institutions should be
able to appeal to a wide range of students by providing them
with the right learning environment to identify and develop
their natural abilities. The “one-size-fits-all” approach to higher
education has long been criticized as outdated, because it focuses
on providing a curriculum that ends up figuring out who
can pass certain subjects and who cannot, ignoring the fact
that not every student has the same amount of knowledge
in all subjects. Higher education can be described as a time-
driven and place-depended system that can lead to learning
gaps that negatively impact the knowledge and skills learners
need in today’s information age (Demski, 2012;Watson et al.,
2012;Alamri et al., 2021). Therefore, higher education faculties
and educators are under constant pressure to transform the
learning experience by focusing on flexible learner-centered
environments that can be personalized to meet the needs,
interests, and preferences of each learner and foster the twenty-
first century skills that are required for information age learners
(Johnson et al., 2016;Alamri et al., 2021). Given today’s
familiarity with e-learning platforms that promise personalized
experiences, students should be allowed to expect some degree
of personalization of their learning experience, forcing higher
education to adapt to the rapidly changing expectations of
students, the realities of society, and the future workplaces of its
graduates (Walkington and Bernacki, 2020).
The personalized learning approach holds promise for
tailoring instruction to individual learning trajectories and
maximizing student motivation, satisfaction, engagement, and
learning efficiency (Liu et al., 2006;Gómez et al., 2014).
Specifically, the U.S. Department of Education, Office of
Educational Technology (2016) defines personalized learning as
“instruction in which the pace of learning and the instructional
approach are optimized for the needs of each learner. Learning
objectives, instructional approaches, and instructional content all
may vary based on learner needs. In addition, learning activities
are meaningful and relevant to learners, driven by their interests,
and often self-initiated” (p. 7). Johnson et al. (2016) use the
term personalized learning to refer to a variety of educational
programs, learning experiences, instructional approaches, and
academic support strategies designed to address the unique
learning needs, interests, aspirations, or cultural backgrounds
of individual students. Newman et al. (2013), on the other
hand, define personalized learning as an educational method or
process that relies on observations to develop tailored educational
interventions for students that increase the likelihood of learning
success. Although a clearly defined concept of personalized
learning is still lacking (Schmid and Petko, 2019), current
definitions of personalized learning adhere to the paradigm
of learner-centered instruction to provide each student with
exactly the type of learning experience they need at a given
time and to ensure that their interests, preferences, expectations,
learning deficits and needs are addressed while they take
responsibility for their own learning progress (Benhamdi et al.,
2017;Jung et al., 2019;Plass and Pawar, 2020). At the moment,
personalized learning functions more as an umbrella term that
describes a range of approaches and models, such as competency-
based learning, self-paced instruction, differentiated instruction,
individualized instruction, and adaptive learning (Newman et al.,
2013;Schmid and Petko, 2019).
Although the use of technology is not mandatory, it greatly
facilitates personalized learning. Especially in higher education
where instructors address classes with high enrollment, any
attempt to personalize learning without learning technologies
would be very challenging. In some learning contexts,
personalized learning can take the form of blended learning,
where both online and face-to-face learning experiences are
put into practice to teach students. For example, in a blended
learning course, students may participate in a course taught by an
instructor in a traditional classroom while completing the online
components of the course outside of the traditional classroom.
In this case, time spent in the classroom can either be replaced or
supplemented by online learning experiences in which students
learn the same material as in the classroom (Garrison and
Kanuka, 2004). Online and face-to-face learning experiences
thus coexist and function in parallel and complementary ways
while increasing the effectiveness and efficiency of meaningful
learning experiences.
The use of adaptive technologies for personalized learning is
not new; computer-based adaptive learning has been around for
more than half a century, beginning in the early 1970s with the
advent of intelligent tutoring systems (Shemshack and Spector,
2020). In its simplest form, the computer program adapts the
learning path based on a student’s responses. However, the
growing interest in tailoring instruction to individual student
needs has led to the development of new technologies that
offer new opportunities for personalized learning (Johnson
et al., 2016). More sophisticated adaptive learning technologies
go beyond simply responding to learner responses. Adaptive
learning technologies, whether computer-based or online, take
a “sophisticated, data-driven, and in some cases, non-linear
approach to instruction and remediation, adjusting to a learner’s
interactions and demonstrated performance level and subsequently
anticipating what types of content and resources learners need at
a specific point in time to make progress” (Newman et al., 2013,
p. 4). The success of any adaptive learning technology to create a
didactically sound and flexible learning environment depends on
accurately diagnosing the characteristics of a particular learner
or group of learners when delivering content by collecting much
more data from learners to better adapt to and support learners’
individual learning journeys (Shute and Zapata-Rivera, 2012).
With the advent of big data, it is possible to capture and
interpret learners’ individual characteristics and real-time state
in all aspects of learning (Peng et al., 2019). These big data and
learning analytics can include prior learning experiences and
performance, learners’ self-expressed preferences for learning
methods, analytical predictions of each learner’s likelihood of
success through different learning methods, personality traits,
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Christodoulou and Angeli Adaptive Learning in Teacher Education
affective states, cognitive types, interests, and so on (Shute
and Zapata-Rivera, 2012;Zhu and Guan, 2013). Then, the
compilation of information about the learner is used as the basis
for prescribing optimal content and real-time individualized
feedback, such as hints, explanations, hypertext links, practice
tasks, encouragement, and metacognitive support (Snow, 1989;
Park and Lee, 2004;Shute and Zapata-Rivera, 2012), all of which
are effortlessly incorporated into the learning activity. Learners
become part of the process of defining learning outcomes,
pedagogies, and practices of the learning experience. Until
recently, it did not seem possible to meet learners’ needs in
this way. The operation of adaptive technologies can be better
understood using the framework of Shute and Zapata-Rivera
(2012). Their framework includes a four-process cycle that uses
a learner model to connect the learner to appropriate educational
materials or resources (e.g., other learners, learning objects,
applications, and pedagogical agents). The four-process cycle
refers to four distinct processes, namely: Capture, Analyze, Select,
and Present. During the “Capture” process, the system first
collects information (e.g., cognitive data such as answers to
a test and/or non-cognitive data such as engagement) about
the learner while interacting with the system. The collected
information is used to update the internal models that the
system maintains. Then, during the “Analyze” process, the system
analyzes the information from the assessments and the resulting
inferences about the skills contained in the learner model to
decide what to do next. This decision is related to adapting and
thus optimizing the learning experience. During the “Selection”
process, information is selected for a particular learner according
to the current state of the learner model and the purpose of the
system (e.g., the next learning object or test task). This process
is often necessary to decide how and when to intervene. Then,
during the “Presentation” process, depending on the results of
the previous process, specific content is presented to the learner
to optimize their learning path. This includes the appropriate
use of media, devices, and technology to successfully deliver
information to the learner.
In response to the resulting increase in market demands
for adaptive learning solutions, more and more entrepreneurial
and innovative services and solutions are emerging that
simultaneously challenge the “traditional” models and providers
of education and learning services. The pressure that higher
education institutions face today is likely to intensify in
the coming years (Newman et al., 2013). Ironically, higher
education, which conducts much of the research on learning
sciences and effective teaching models, lags far behind K-12
and corporate markets in applying the lessons learned (Newman
et al., 2013). Recently, however, interest has emerged among
higher education educators in exploring the use of adaptive
learning systems to address the individual learning needs and
characteristics of students entering formal academic programs
(Foshee et al., 2016). The increase in the number of research
articles published in high-impact educational technology journals
between 2007 and 2017 that address adaptive or technology-
enhanced personalized learning indicates that it is gaining
traction in higher education (Xie et al., 2019). The main obstacle
is that scientific, data-driven approaches that enable effective
personalization of learning have only recently become available.
Adaptive learning to personalize student learning pathways
within higher education is still in development and there is little
evidence-based research on how to develop or implement it (Liu
et al., 2006;Shemshack and Spector, 2020).
To this end, the authors herein aim to contribute to this
line of research by discussing the design and utilization of
e-TPCK, a self-paced adaptive electronic learning environment
that was developed and used to support the development of
student-teachers’ Technological Pedagogical Content Knowledge
(TPCK) in a personalized way during their undergraduate
studies. According to Angeli and Valanides (2009), TPCK forms
a unique body of knowledge that is better understood in
terms of competencies that teachers must develop to teach
appropriately with technology. Specifically, according to Angeli
and Valanides (2009), the construct of TPCK is defined in terms
of the following five competencies: (a) identify topics to teach
with technology that are not easily comprehensible to learners
or difficult to teach, in ways that signify the added value of
technological tools, (b) identify representations for transforming
the content to be taught into forms that are understandable
to learners and difficult to by aided by traditional means, (c)
identify teaching approaches that are difficult or impossible to
be implemented by traditional means, (d) select technological
tools which incorporate inherent features to provide content
transformations and support teaching approaches, and (e) infuse
technology-enhanced learning activities in the classroom.
The e-TPCK system employs a technological solution that
promotes the ongoing development of student-teachers’ TPCK
by engaging them in personalized learning experiences using
technology-enhanced design scenarios, taking-into-account
their diverse needs, information processing limitations, and
preferences. The system was first designed and developed, and
then integrated as a learning tool in a face-to-face Instructional
Technology course that aimed to teach pre-service teachers
how to teach with technology. The authors, who were also
the course instructors, used e-TPCK as a complement to the
course to engage pre-service teachers in technology-enhanced
design experiences. This type of engagement allowed pre-service
teachers to successively refine their thinking about learning
design with technology by making explicit the connections
and interactions among content, pedagogy, technology,
learners, and context.
Accordingly, the present study assumed an experimental
research design in order to examine the extent to which
learning with e-TPCK had a statistically significant effect on
the development of TPCK between pre-service teachers who
interacted with it to learn how to design technology-enhanced
learning and those who did not use it as part of their learning
during a 13-week semester course in educational technology.
MATERIALS AND METHODS
Participants
The sample of the study consisted of an experimental and
a control group of student-teachers taking a compulsory
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Instructional Technology course at the undergraduate level.
In the study, one group constituted the experimental group
that used e-TPCK while participating in the course, while the
other group constituted the control group. One hundred and
nineteen participants constituted the experimental group and
used e-TPCK to learn about how to design technology-enhanced
learning, while 87 participants constituted the control group and
received the conventional treatment without the use of e-TPCK,
one semester earlier. Participants in the control and experimental
groups were first- and second-year student-teachers, who had
basic computer skills but no experience in instructional design
using technology. Both groups attended 13 weekly lectures and
13 weekly labs, used the same course materials and readings,
had the same instructor for the lectures and the same teaching
assistant for the labs, received the same pre-test and post-test, and
were assessed in the same way. The only difference was that the
experimental group was purposefully exposed to activities related
to the design of technology-infused teaching and learning using
the e-TPCK learning environment at home.
In contrast, the control group did not use e-TPCK during the
semester. However, students in the control group had access to
all learning materials that students in the experimental group
accessed through e-TPCK. These were uploaded in an electronic
classroom in Blackboard but without having the built-in adaptive
feature of the e-TPCK system.
Description of the System
The design and development of e-TPCK was the result of three
iterative cycles of a design-based research project. The design
of the system incorporated aspects of approaches and theories
such as learning-by-design, personalized and adaptive learning,
cognitive load theory, scaffolding, and self-regulated learning.
The e-TPCK system was not designed as an electronic learning
system that simply delivered content to student-teachers. Rather,
e-TPCK was a cognitive partner that supported student-teachers’
learning and enabled them to reach the next stages of their
TPCK development in a self-paced manner while the system was
personalizing the content to them in the form of technology-
infused learning design scenarios. Essentially, e-TPCK consists
of three main learning spaces, namely, (a) Learning Material (as
shown in Figure 1), (b) Social Networking Tools (as shown in
Figure 2), and (c) Instructional Design (as shown in Figure 3).
The study presented herein addresses the learning experiences
of student-teachers in the Instructional Design space only. The
Learning Material learning space was a digital library that
contained information and materials to help student-teachers
develop an understanding of instructional design and technology
integration, while the Social Networking Tools learning space
contained Web 2.0 technologies that enabled synchronous and/or
asynchronous communication between student-teachers and/or
the instructor to negotiate ideas about the design of technology-
enhanced teaching and learning via chat or forum. The main
learning space of the e-TPCK system was the Instructional Design
space, where student-teachers learned, practiced, and gradually
developed their TPCK. This learning space included three types
of learning design scenarios with different levels of difficulty.
The learning scenarios aimed to guide student-teachers through
a sequence of instructional design decisions on how to teach a
particular topic using technology. Specifically, e-TPCK included
(a) completed (worked-out) learning design scenarios (difficulty
level = 0) that represented sound examples of instructional
design using technology for a specific content to be taught, (b)
semi-completed learning design scenarios (difficulty level = 1–
4) that lacked some phases in the sequence of learning activities
and needed to be completed by student-teachers, and (c) new
learning design scenarios (difficulty level = 5) that student-
teachers had to develop on their own using the same format as
in the completed design scenarios, and submit them in the system
when finished for formal assessment by the instructors. The semi-
completed learning design scenarios were ranked from simple
(difficulty level = 1) to complex (difficulty level = 4), according
to the number of phases missing from the sequence of learning
activities. The more phases student-teachers had to complete,
the more difficult the design task was. Accordingly, the easiest
task was the completed design scenarios, while the new design
scenarios were the most difficult design tasks for the student-
teachers. Specifically, the structure of each technology-enhanced
design scenario was as follows:
1. Rationale of topic selection and technology’s added value.
2. Subject-matter content description, including connections
with the curriculum.
3. Learning objectives (lower-order learning
objectives, higher-order learning objectives, and
technology-related objectives).
4. Sequence of classroom activities:
Phase 1: Gain attention/attract student interest.
Phase 2: Identification/diagnosis of learners’ initial
perceptions or misconceptions/alternative conceptions.
Phase 3: Destabilization of initial perceptions through the
induction of cognitive conflict.
Phase 4: Construction of new knowledge and
active engagement of learners in the knowledge
construction process.
Phase 5: Application of new knowledge in a new context.
Phase 6: Revision and comparison with initial ideas.
5. Final student assessment.
The e-TPCK system provided a personalized learning
experience by adapting (a) the user’s learning path based
on his/her subjective assessment of cognitive load, (b) the
user’s preferences for technological tools, and (c) the difficulty
level of the scenario. The adaptation strategy consisted of the
following elements: (1) Adaptation Parameters, such as the
learners’ perceived cognitive load, the choice of technological
tools in the design scenario, and the difficulty level of the
technology-enhanced design scenario as determined by the
instructional designers of e-TPCK, (2) Adaptation Type, namely,
the adaptation of the content, the flow of learning, and the
sequence of activities, and (3) Adaptation Rules, such as
conditional rules that assign and implement shared control
between the system and the end user.
The system asked the student-teachers to choose the difficulty
level of the learning design scenario in an attempt to adjust the
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FIGURE 3 | Instructional design space.
content to their learning needs and preferences. The adaptation
process was facilitated by the cognitive load monitor (CLM), as
shown in Figure 4. Once users started with a completed or semi-
completed design scenario, they had the opportunity to read the
first six elements of the design scenario just before the sequence of
learning activities began. At that point, the CLM was activated by
asking users to indicate the level of cognitive effort they perceived.
Student-teachers’ subjective ratings of perceived cognitive effort
were measured using a 7-point Likert scale ranging from very
very low to very very high cognitive effort. Depending on the
users’ self-assessment of their mental effort, the system shared
control with them by allowing them to choose the next step
from a list of options provided by the system. Specifically,
in cases where users had indicated a high level of cognitive
effort, the system asked them if they wanted to proceed with
a less difficult design scenario. If the users answered positively,
the system gave them a less difficult design scenario with the
same technological tool or less difficult design scenarios with
different technological tools that had a lower level of difficulty.
However, if the users answered negatively, they continued with
the same design scenario. In the case where users indicated
low cognitive effort, the system asked them if they would like
a more difficult design scenario. If they answered positively,
the system gave them a more difficult design scenario with the
same technological tool or more difficult design scenarios with
different technological tools that had a higher level of difficulty.
However, if users answered negatively, they continued with the
same design scenario. Essentially, e-TPCK involved instances
of shared instructional control, where adaptive behavior was
controlled by both the learner and the system.
If student-teachers chose to remain with the same completed
or semi-completed design scenario, before continuing with
e-TPCK, they needed to take a short test that allowed the system
to provide them with adaptive feedback. If the users’ performance
on the test was equal to or above 50%, they could proceed to
the next part of the design scenario. If their performance was
below 50%, they remained on the same page and were asked
to read it again. On the left side of the user interface, the
system displayed the description of the learning material and
the learning objectives for each design scenario. Users could
decide whether to revise or ignore this type of information. If
users successfully completed all five tests of the first completed
design scenario, they were allowed to proceed to the first semi-
completed design scenario. The same procedure was followed
for the first semi-completed design scenario. After successfully
completing the five tests, users were allowed to proceed with
developing a new design scenario from scratch. In both cases, if
the users chose to continue with the same type of design scenario,
i.e., completed or semi-completed, the system gave them the
option to select a more difficult design scenario using the same
or a different tool.
Scaffolds were an important feature in the design and
development of e-TPCK, as shown in Figure 5.
Scaffolds are tools, strategies, or instructions that the system
provided during the learning process to help learners reach
higher levels of understanding that they could not achieve
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Christodoulou and Angeli Adaptive Learning in Teacher Education
on their own (Hannafin et al., 1999;Saye and Brush, 2002;
Azevedo and Hadwin, 2005). Student-teachers’ learning paths
were scaffolded only for completed or semi-completed learning
design scenarios, while all scaffolding was hidden for new
scenarios to assess how well student-teachers could perform
without any scaffolding. Scaffolds in e-TPCK provided cognitive,
conceptual, metacognitive, procedural, or strategic support.
For example, while completing the missing elements of a
semi-completed design scenario, student-teachers encountered
conceptual scaffolds in the form of pop-up windows to promote
their understanding of the elements of constructivism as they
appeared in the learning design scenarios. Student-teachers could
simply close and ignore the pop-up windows if they found
them unhelpful or distracting. Also, e-TPCK informed student-
teachers about the general goal of each learning session within
the Instructional Design learning space, and also assisted them
in defining their own learning sub-goals of their learning.
The presence of goals and sub-goals constituted metacognitive
scaffolds that provided student-teachers with guidance about
what to consider in terms of the task and the context of the
task, and what to expect in terms of learning once the task
was completed. This supported student-teachers’ monitoring and
reflective processes by increasing their metacognitive awareness
of the different aspects of their learning (the task, the context,
the self in relation to the task). Another example of conceptual
scaffolding was the information resources that were always
available on the left side of the screen to help student-teachers
fill in essential knowledge gaps between what they knew and
what they needed to know in relation to the design task. In
addition, previously visited pages or other sources of information
were marked facilitating student-teachers’ future access to the
same information.
In general, the interface of the Instructional Design
learning space prompted student-teachers to engage in
planning, monitoring, controlling, and reflecting or adapting
different strategic learning behaviors. Also, this learning
space fostered a variety of self-regulated learning behaviors,
including prior knowledge activation, goal setting, planning,
evaluation of learning strategies, integrating information across
representations, content evaluation, and note-taking.
Research Instruments
The following questionnaires were used to collect data from the
participants of the study: (1) a demographics questionnaire, (2) a
researcher-made pre-test that assessed pre-service teachers’ initial
instructional design skills at the beginning of the semester, and (3)
a post-test (same as pre-test) that was administered at the end of
the semester to examine pre-service teachers’ final instructional
design skills. Additionally, the researchers assessed students’ total
TPCK by evaluating their performance on two design artifacts
that student-teachers completed around the 8th and 13th weeks
of the semester, respectively.
The demographics questionnaire was administered to both
groups of participants during the first lab meeting of the semester.
The questionnaire consisted of only a few questions for the
purpose of collecting data related to students’ name, surname,
age, and academic status (i.e., undergraduate student).
The pre-test consisted of 10 questions and assessed student-
teachers’ skills on instructional design and it was administered at
the beginning of the course. The same test was administered as
post-test at the end of the semester. Student-teachers’ total score
on the pre-test was regarded to be their initial instructional design
competency, whereas their total score on the post-test at the end
of the semester was regarded to be their final instructional design
competency. All questions on the test were graded on a scale from
0 to 10, thus the maximum score on the test was 100 points.
In addition, the researchers assessed the student-teachers’
overall TPCK competency by assessing their design performance
on two design artifacts that the student-teachers developed
from scratch during the 8th and 13th weeks of the semester.
The overall TPCK competency for the two design artifacts was
assessed by the two course instructors using Angeli and Valanides’
(2009) five individual TPCK competencies. Interrater reliability
for the first design artifact was determined to be r= 0.96,
and r= 0.94 for the second design artifact. Each of the five
TPCK competencies was rated on a 0–20-point scale. The sum
of the five TPCK competencies resulted in the total TPCK
competency. Thus, a total score of 100 points indicated an
excellent overall TPCK score.
Research Procedures
At the beginning of the semester, student-teachers in the control
and experimental groups took the pre-test consisting of 10
scenario-based questions that tested their initial competencies
in instructional design with technology. The same test was
administered at the end of the semester as a post-test to
assess final instructional design competencies. In addition,
the researchers assessed the student-teachers’ overall TPCK
competency by assessing their design performance on the two
design artifacts that the student-teachers developed from scratch
during the 8th and 13th weeks of the course.
During the lectures, student-teachers learned about
instructional design according to the TPCK competencies,
the added value of technological affordances and how these
connect to content and pedagogical transformation, learning
theory and learning design. During lab time, student-teachers
learned how to use various technological tools and were engaged
in instructional design activities using these tools. All participants
in the study were engaged in instructional design activities using
technology that included completed and semi-completed
learning design scenarios. However, only the experimental group
had the opportunity to use the e-TPCK system at home.
RESULTS
Pre-service Teachers’ Initial and Final
Assessment of Instructional Design
Knowledge
At the beginning of the semester, pre-service teachers in each
group were administered a researcher-made pre-test to measure
initial instructional design knowledge. The pre-test was consisted
of 10 questions and aimed at assessing students’ knowledge in
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terms of designing learning activities based on the constructivist
learning model. All questions on the test were graded on a scale
from 0 to 10, thus the maximum score on the test was 100
points. At the end of the semester, the pre-test was administered
as post-test in order to determine learning gains between the
two time points.
A number of statistical tests were conducted to analyze the
data. The data met the assumptions of all statistical tests the
authors of the study performed. First, a one-way between-
subjects ANOVA was conducted to examine whether pre-
service teachers’ initial performance on the instructional design
knowledge test was different for the control and the experimental
groups. The results determined that there was a statistically
significant difference between the two groups, F(1,206) = 47.23,
p<0.01, as the experimental group (M= 57.24, SD = 15.19)
performed much higher on the pre-test than the control group
(M= 44.13, SD = 11.17).
Accordingly, in order to eliminate the initial differences
between the two groups, the participants in the control and
experimental conditions, were further divided into two sub-
groups according to their expertise level as assessed by the
instructional design knowledge test. Pre-service teachers who
scored between 0 and 49 points were assigned to the novices’
group, while those who scored between 50 and 100 points were
assigned to the experts’ group. There were 57 novices and 32
experts in the control group, and 36 novices and 83 experts in
the experimental group.
Table 1 shows the descriptive statistics of pre-service
teachers’ performance on the pre-test, both in the control
and experimental groups. Experts, in both groups, performed
higher than the novices. Furthermore, all participants in the
experimental group, novices and experts, performed better than
their respective counterparts in the control group. The novice
participants in the experimental group performed slightly better
on the test than their counterparts in the control group, with a
mean difference of 1.26 points (SD = 6.95), while the experts in
the experimental group had a substantial mean difference of 9.93
points (SD = 10.32) from their counterparts in the control group.
Table 2 presents the descriptive statistics of pre-service
teachers’ performance on the post-test, per intervention group
and level of expertise. Once again, experts in the control group
(M= 70.18, SD = 15.66) and the experimental group (M= 73.80,
SD = 11.43) appeared to have higher scores on the post-test
questions than novices in the corresponding groups (M= 56.83,
SD = 14.72) (M= 66.72, SD = 12.07). However, both novices
and experts in the experimental group performed better than
TABLE 1 | Descriptive statistics of pre-service teachers’ performance
on the pre-test.
Expertise Control group Experimental group Total
Mean SD NMean SD NMean SD N
Novices 37.95 8.46 57 39.21 6.95 36 38.44 7.89 93
Experts 55.13 5.45 32 65.06 10.32 83 62.30 10.24 115
Total 44.13 11.17 89 57.24 15.19 119 51.63 15.06 208
their counterparts in the control group. Analytically, novices
and experts in the control group had an improvement of 18.88
and 15.05 points on the post-test, respectively, while novices
and experts in the experimental group scored 27.51 and 8.74
points higher on the post-test, respectively. Apparently, all
participants performed better on the post-test due to the teaching
intervention; however, the novices in both groups showed greater
improvement than the experts in each group. More importantly,
though, the novices in the experimental group had the greatest
improvement of all. Specifically, on the post-test, novices in
the experimental group managed to increase the post-test mean
difference between their counterparts in the control group by
9.89 points, while on the pre-test their mean difference was only
1.26 points. Experts in the experimental group, on the other
hand, showed the exact opposite. Although, on the pre-test, the
mean difference between the experts’ performance in the control
group, and the experts’ performance in the experimental group
reached 9.93 points, on the post-test, this mean difference was
eliminated to 3.62 points.
A follow-up two-way ANOVA was conducted to determine
statistically significant differences between the control and
experimental groups. The results showed statistically significant
main effects for “expertise” and “treatment condition” on
students’ performance on the post-test, F(1,204) = 27.02,
p<0.001, partial η2= 0.12 and F(1,204) = 11.80, p<0.001,
partial η2= 0.06, respectively, in favor of the experimental group.
Pre-service Teachers’ Total
Technological Pedagogical Content
Knowledge Competency
Pre-service teachers’ total TPCK competency was measured on
two different occasions using two design artifacts. For each design
artifact, pre-service teachers had to identify topics to be taught
with technology in ways that signify the added value of the tools,
such as topics that learners cannot easily comprehend, or topics
that teachers face difficulties in teaching effectively in class. The
first design artifact was completed during the 8th week of the
semester and the second on the 13th week of the semester. Hence,
the overall elapsed time between the two design tasks was 5 weeks.
The total TPCK competency on the two design artifacts
was measured by the two instructors using the five individual
TPCK competencies reported by Angeli and Valanides (2009),
namely: (a) Identification of appropriate topics to be taught with
technology in ways that signify the added value of the tools,
such as topics that students cannot easily understand, or content
TABLE 2 | Descriptive statistics of pre-service teachers’ performance
on the post-test.
Expertise Control group Experimental group Total
Mean SD NMean SD NMean SD N
Novices 56.83 14.72 57 66.72 12.07 36 60.66 14.52 93
Experts 70.18 15.66 32 73.80 11.43 83 72.79 12.78 115
Total 61.63 16.30 89 71.65 12.02 119 67.37 14.84 208
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Christodoulou and Angeli Adaptive Learning in Teacher Education
that teachers have difficulties in teaching effectively in class,
(b) Identification of appropriate representations to transform
content to be taught into forms that are comprehensible to
students and difficult to be supported by traditional means, (c)
Identification of teaching strategies difficult or impossible to be
implemented by traditional means, (d) Selection of appropriate
tools and effective pedagogical uses of their affordances, and
(e) Identification of appropriate integration strategies, which are
learner-centered and allow learners to express a point of view,
observe, inquire, and problem solve.
The interrater reliability for the first design artifact was
calculated to be r= 0.96 while for the second design artifact
was found to be r= 0.94. Each of the five TPCK competencies
was graded on a scale from 0 to 20 points for a total of
100 points denoting the total TPCK competency. Overall, the
control and the experimental groups experienced the same design
tasks, followed the same instructions, guidelines, and assessment
process. However, the experimental group had the opportunity,
before and between the two design tasks, to further practice in
instructional design using the learning environment of e-TPCK.
According to Table 3, all participants in the experimental
group, both novices and experts, performed better than their
counterparts in the control group on the first design artifact.
Novices in the experimental group attained a mean score of
73.25 points (SD = 7.42), performing considerably higher than
their counterparts in the control group (M= 64.77, SD = 12.15)
with a mean difference of 8.48 points. Experts in the experimental
group, on the other hand, had a higher performance (M= 76.45,
SD = 8.59) than the experts in the control group (M= 73.19,
SD = 9.60) with a mean difference of 3.26 points. Experts in
the control group attained a higher mean score of 8.42 points
than the novices in the control group, while the experts in the
experimental group had a lower mean difference than the novices
(3.20 points) in the same group. The most notable finding is the
fact that the novices in the experimental group performed not
only better than their counterparts in the control group, but also,
better than the experts in the control group. This shows that
engagement with e-TPCK improved the novices’ performance on
the first design artifact.
The results appear to be almost the same with regards to
the second design artifact with novices and experts in the
experimental group attaining higher mean scores than their
counterparts in the control group. Novices in the experimental
group achieved a mean score of 73.83 (SD = 10.31), while novices
in the control group performed 10.08 points lower (M= 63.75,
TABLE 3 | Descriptive statistics of pre-service teachers’ total TPCK competency.
Condition Expertise First design artifact Second design artifact
Mean SD NMean SD N
Control group Novices 64.77 12.15 57 63.75 10.81 57
Experts 73.19 9.60 32 73.91 10.76 32
Experimental
group
Novices 73.25 7.42 36 73.83 10.31 36
Experts 76.45 8.59 83 75.05 10.79 83
SD = 10.81) on the second design task. The novices’ performance
in the experimental group remained approximately the same
as that on the first design task. Experts in the experimental
group, on the other hand, attained a mean score of 75.05 points
(SD = 10.79), surpassing their counterparts in the control group
by 1.14 points (M= 73.91, SD = 10.76). The experts in the control
group performed minimally higher on the second design task
(0.72 points) compared to their performance on the first design
task. Experts, though, in the experimental group performed
slightly below their mean score on the first design task (1.40
points). Hence, experts’ mean score difference, in the control
and experimental groups, was slightly decreased by 2.12 points
on the second design task. Overall, novices and experts, in both
groups experienced minimal fluctuations in their performance
on the second design task when compared to their performance
on the first one.
A two-way between-subjects repeated measures ANOVA was
conducted to compare the main effects of “condition” (i.e., being
in the control or the experimental group) and “expertise” as
well as detect an interaction effect between these two factors on
pre-service teachers’ performance on the two design artifacts.
A statistically significant between-subjects interaction was found
between expertise and condition variables, F(1,204) = 7.49,
p<0.05, partial η2= 0.04, for the first design task. The
nature of this interaction effect is shown in Figure 6. Simple
main effects were also run. Both main effects of condition and
expertise determined that there was a statistically significant
difference in pre-service teachers’ performance between the two
time points, yielding an F ratio of F(1,204) = 19.71, p<0.05 and
F(1,204) = 19.74, p<0.05, respectively.
Accordingly, a statistically significant between-subjects
interaction was also found between expertise and condition
variables, F(1,204) = 6.53, p<0.05, partial η2= 0.08, for
the second design task. The nature of this interaction effect
is shown in Figure 7. Simple main effects were also run.
Both main effects of condition and expertise determined that
there was a statistically significant difference in pre-service
teachers’ performance between the two time points, yielding
an F ratio of F(1,204) = 18.61, p<0.05 and F(1,204) = 18.74,
p<0.05, respectively.
These results show that learning with e-TPCK provided a
more facilitating effect for novices in the experimental group that
contributed to their high scores on the two design artifacts, fast
approaching the performance of the experts in the experimental
group and attaining almost the same performance with the
performance of the experts in the control group. Evidently, the
positive effect of e-TPCK, when integrated in students’ teaching
and learning, was manifested mainly in the performance of the
novices in the experimental group. On the contrary, the experts
in the experimental group tended to score lower on the second
design artifact demonstrating that the additional instructional
guidance they received by interacting with e-TPCK was not
necessary or even detrimental for them. This notable result can be
well explained and understood through the concept of expertise
reversal effect. Although advantages of individualized learner-
tailored instruction are recognized, it is shown that procedures
and techniques designed to increase levels of instructional
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FIGURE 6 | The interaction effect between expertise and condition on the first design artifact.
FIGURE 7 | The interaction effect between expertise and condition on the second design artifact.
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Christodoulou and Angeli Adaptive Learning in Teacher Education
guidance were more effective for novice learners. However,
the instructional guidance that is essential for novices may
inhibit learning for more experienced learners, as the results
of the present study indicated, by interfering with retrieval
and application of their available knowledge structures (Kalyuga
and Renkl, 2010). Therefore, unnecessary guidance should be
removed as learners acquire higher levels of proficiency in a
specific domain during the course of their learning with an
adaptive computer system.
DISCUSSION
Based on the results of the current study, the integration of
adaptive e-learning systems in educational technology courses
can foster the development of pre-service teachers’ TPCK faster
than traditional instruction that depends solely on face-to-face
meetings with no use of adaptive computer technology. The
experimental group outperformed the control group in both
design tasks, demonstrating the potential of e-TPCK to support
student-teachers’ TPCK development in more efficient, effective,
and individualized ways. What is more, e-TPCK provided
a more facilitating learning effect for some students in the
experimental group, whereas for some others this was not
the case. The instructional guidance that e-TPCK offered for
TPCK development, led to a mathemagenic effect for student-
teachers who were regarded as novices in terms of their initial
instructional design competency, whereas for student-teachers
who were regarded as more experienced, it had more of a neutral
effect and, in some cases, a mathemathantic effect. Although both
experts and novices benefited from learning with e-TPCK during
the initial weeks of the semester, later only the novices continued
to experience learning gains, while the experts’ performance
decrease leading to an expertise reversal effect. Interestingly, the
novelty effect did not affect the novices as they continued to learn
throughout the semester. In contrast, the novelty effect affected
the more experienced ones as they found the system beneficial
only during the first weeks of the semester but not throughout
the entire semester.
The expertise reversal effect is a reversal in the relative
effectiveness of instructional methods and procedures as levels of
learner knowledge in a domain change (Kalyuga, 2007;Kalyuga
and Renkl, 2010). Instructional techniques that assist novices
or low-experienced learners can lose their effectiveness, or even
adverse effects when learners acquire more expertise (Sweller
et al., 2003;van Merrienboer and Ayres, 2005;Kalyuga, 2007;
Kalyuga and Renkl, 2010). Hence, a learner’s level of expertise
(the one’s ability to perform fluently in a class of tasks) is a crucial
factor determining what information is relevant for the learners
and what instructional guidance is needed (Kalyuga, 2007).
The expertise reversal effect has been replicated in many
research studies of different domains and with a large range of
instructional means (e.g., tasks that require declarative and/or
procedural knowledge in mathematics, science, engineering,
programming, social psychology, etc.) and learners (from
primary to higher education levels), either as a full reversal
(a disordinal interaction with important differences for both
novices and experts) or, more often, as a partial reversal (non-
significant differences for novices and experts, but with a
significant interaction), stressing the need to adjust instructional
methods, procedures, and levels of instructional guidance as
learners acquire higher levels of proficiency in a particular
domain (Kalyuga, 2007). Appropriate instructional support
should be provided to novice learners to build new knowledge
structures in a relatively efficient way, while unnecessary
guidance should be removed as the learners become more
knowledgeable in a domain.
The design of e-TPCK attempted to provide adaptive
scaffolding to student-teachers by personalizing the content to
them to support their learning, acknowledging the advantages of
individualized learner-tailored instruction. For both completed
and semi-completed design scenarios, several levels of adaptive
support were provided in terms of the degree of completeness
and instructional hints. The fading from one phase to the next
(from completed design scenarios to semi-completed or new
design scenarios) was facilitated through the CLM. Based on
the subjective ratings concerning learners’ perceived cognitive
effort about a design scenario, the system was adapting the
learning path of its users, accordingly, as to reduce extraneous
cognitive load and not to waste limited resources that could be
used for effort. Subsequently, the student-teachers’ learning path
was adapted according to their preferences on the technology
tools used in the design scenarios, and/or the difficulty level
of the design scenarios, by sharing control with them and
giving them the opportunity to determine their next step in the
learning process. Consequently, adaptive fading in e-TPCK was
not employed based on the actual knowledge growth of each
learner over time; instead, it was primarily based on the mental
effort approach (Paas and Van Merrienboer, 1993) and, then
on their preferences in the technological tools in a way that all
learners could work on their personal learning pace and gradually
guided from the easiest to the most complex design task through
the fading strategy. The adaptation strategy employed in the
system constituted a more learner-controlled adaptation strategy,
and an alternative to system- controlled tailoring of instruction.
However, as many researchers (Kalyuga, 2007;Sweller et al.,
2011) argue, it is not always correlated with positive learning
outcomes; instead, this approach can be advantageous only if
the learners have reached that level of expertise that allows
them to select appropriate learning strategies to their cognitive
states, determining their next step in the learning session. This
significant conclusion calls for changes in the instructional
design of e-TPCK, addressing learners’ knowledge base as the
most important cognitive characteristic that affects learning and
performance, as to ensure that learners will not experience the
expertise reversal effect. Kalyuga (2007) critiqued several projects
in adaptive e-learning that focused primarily on technical issues
of tailoring content to learner preferences, interests, selections
and history of previous online behavior, rather than taking-into-
account fundamental cognitive characteristics such as the level
of expertise of the learners. Therefore, e-TPCK should examine
the employment of a hybrid approach that would describe the
task selection rules for its users. Performance or level of expertise
and cognitive load could be combined and measured at the same
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Christodoulou and Angeli Adaptive Learning in Teacher Education
time while the CLM is being activated. This approach would
encourage the instructional design of e-TPCK with presumable
learning advantages for all student-teachers, novices and experts,
in terms of TPCK development.
Lastly, the authors would like to address three limitations of
the study. The first one is related to the fact that the researchers
also acted as the instructors of the course. To eliminate any
threats to the study’s internal validity, the authors report that all
research procedures for each of the 13 lectures and the 13 labs
were written down and followed strictly to cope with any possible
threats. A second limitation is the technical errors students often
had to face while learning with e-TPCK. While the system was
well developed, the authors often received students’ complaints
about technical errors that prohibited them from advancing
from one step to another. Lastly, a third limitation is related
to the instructional design of the e-TPCK. As the system did
not diagnose progress from one level of expertise to a higher
one, it did not recommend a different set of scaffolds to the
more expert learner. In the future, the authors could change the
system’s algorithm to suggest a different set of scaffolds to learners
according to their current level of expertise.
CONCLUSION
The results presented herein are encouraging and can serve as
baseline data for future empirical studies on the integration of
adaptive learning systems in teacher education or professional
development courses for teachers. The results of the study
provide insights for researchers or higher education instructors
interested in implementing personalized learning with the use
of adaptive technologies in their classrooms to support their
students in the learning process. The results of the study can
be used to refine the design of such environments, address
new or emerging issues, support new theories or approaches to
strengthen understanding of the research and practice of such
adaptive personalized learning environments and guide further
research and theory development.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
Ethical review and approval was not required for the study on
human participants in accordance with the local legislation and
institutional requirements. The patients/participants provided
their written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
All authors listed have made a substantial, direct, and intellectual
contribution to the work, and approved it for publication.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
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Frontiers in Education | www.frontiersin.org 14 June 2022 | Volume 7 | Article 789397
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