Individualizing Learning Pathways with Adaptive Learning
Strategies: Design, Implementation and Scale
Ana Donevska-Todorova
, Katrin Dziergwa
and Katharina Simbeck
University of Applied Sciences HTW Berlin, Treskowallee 8, 10318 Berlin, Germany
Keywords: Individualized Learning Paths (ILP), Adaptive Learning Strategies, Feedback Adaptations, Adaptive
Educational Systems, Learning Management Systems (LMS), Microlearning, Task Design, Task Sequence,
Design Research (DR), University Education, Applied Mathematics, e-Learning, COVID-19 Pandemic.
Abstract: Individual undergraduate learners have heterogeneous knowledge backgrounds and undergo diverse learning
experiences during their university studies. Consequently, designs of virtual learning environments should
adjust to learners’ needs and competencies, especially in the current pandemic crisis. This paper discusses
pedagogical aspects of personalized and self-regulated learning and situates its focus on design,
implementation, and scale of e-content and e-activities for individualized learning pathways (ILP).
Characteristics of ILP such as shape, length, and turning points enabled through adaptive features of existing
Learning Management Systems (LMS) have seldom been discussed in the literature. We tackle this issue from
a didactical perspective of microlearning with regards to three adaptive learning strategies: 1) Feedback
Adaptations, 2) Task Design, and 3) Task Sequence Design. Within a first phase of a complete initial Design
Research (DR) cycle, we have collected and analysed data which enable us to generate, cluster and label
queries and differentiated items for each of the three strategies. Further on, we offer a visualization of possible
ILP illustrated with contextual examples of productive, technology-based task and feedback designs
applicable and scalable in higher education settings.
The big number of students and their diversity in
background knowledge challenges university leaders
and teaching staff to provide learning opportunities
that are various and flexible in content, time, and
space. During the emergency remote teaching phase
of the COVID-19 pandemic outbreak, academic
educators urged themselves to create e-learning
content and digital activities for inhomogeneous
groups of students. The dynamicity of change and
digitalization accelerated through the pandemic led to
responses that were rapid, but not always of high
quality for learners in asynchronous distant or hybrid
learning contexts. Many of the produced e-learning
contents seem now to be sporadic, unstructured, and
isolated one from another. The unprecedented
demand for automated tasks and digitally generated
activities such as e-tests for autonomous learning has
grown so promptly that it far outperforms the current
To approach such research problem, we dedicate
ourselves to (re-)create, implement, and scale
curricular e-elements of university courses, which
will enable students to gain content-specific and
personal competencies in their own studying tempo.
In doing so, we consider development of learning
opportunities at a macro level (e.g., as by Morze,
Varchenko-Trotsenko, Terletska, & Smyrnova-
Trybulska, 2021) in the frame of a course curriculum
or several courses’ curricula and at a micro level
within a task, a task sequence, and an e-activity. This
proposition for distinction enables concretization and
detailed description of the subject-specific and
didactical appearances of the ILP. In the constrains of
this paper, we focus on design and adaptively aspects
related to microlearning. Microlearning is related to
learners’ engagement in low degrees of time
Donevska-Todorova, A., Dziergwa, K. and Simbeck, K.
Individualizing Learning Pathways with Adaptive Learning Strategies: Design, Implementation and Scale.
DOI: 10.5220/0010995100003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 2, pages 575-585
ISBN: 978-989-758-562-3; ISSN: 2184-5026
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
consumption and consists of micro-content and
micro-activities (Lindner, 2006) that can be
distributed across LMS and Web 2.0 technologies
(Grevtseva, Willems, & Adachi, 2017, p. 132).
Firstly, the paper presents a specification of the
identified terminology in the literature regarding the
variety of learning types like personalized learning,
adaptive learning, and individualized learning. It
then continues with explanations about adaptive
learning strategies that can secure opportunities for
learning in individually chosen paths. This literature
review suggests that there is a growing research body
justifying the need for individualized approaches, but
there is vague evidence of how individualized digital
learning trajectories (may) look like in practice,
which are their characteristics and didactical
The paper further expands around the questions:
which adaptive learning strategies and what kind of
tasks can be designed and applied for supporting
individualization of students’ learning pathways on a
micro level and outlines results of a pre-study in a first
cycle of a Design Research methodological approach.
Shortly summarized, this paper contributes to the
design and research body about adaptive
microlearning with existing Learning Management
Systems (LMS) in the following way. It considers
three adaptive learning strategies for individualizing
students’ learning trajectories:
Feedback Adaptations,
Task Design and
Task Sequence Design.
Moreover, it shows how we have:
created item responses for various types of
feedback adaptations for tasks and task
sequence designs encouraging ILP, analysed in
connection to relevant literature,
generated, clustered and labelled queries and
differentiated entries for task design supporting
described requirements and characteristics of
ILP and their visualization,
offered contextualized and implemented
examples of productive, technology-based
feedback and task designs for ILP in higher
suggested ways for scale and further sustainable
re-design of micro-content and micro-activities for
The modern learner in higher education needs
dynamic learning contents and educational activities
that can be adjusted according to an individual
rhythm of learning. Personalized learning, primarily
mentioned by the Organisation for Economic Co-
operation and Development OECD (2006) is
characterized by changes concerning several aspects
such as assessment providing individual feedback
related to learning objectives, teaching, and learning
strategies referring to the individual needs,
curriculum adoptions, and student-centered
approaches (Shemshack & Spector, 2020).
Personalized learning is also defined through the
instructors’ perspective as an approach that optimizes
pieces of content and their sequencing, and
engagement with this content according to the
requirements, interests and self-initiation of each
learner following learning objectives (U.S.
Department of Education, Office of Educational
Technology (2017). “Personalized learning considers
students’ interests, needs, readiness, and motivation
and adapts to their progress by situating the learner at
the centre of the learning process” (Shemshack &
Spector, 2020, p. 5). Most of the current research
acknowledges the role of technology in supporting
personalization of learning processes. In this regard,
adaptive learning as a way of learning that tries to
best familiarise with learner’s strengths and
weaknesses and accordingly regulate the learning
processes of the individual with digital tools is
perceived to be appropriate for increasing the chances
of success. Further, a recent systematic literature
review for Information Systems Research
distinguishes between personalized learning,
adaptive learning, individualized instruction, and
customized learning (Shemshack & Spector, 2020).
On the one hand, individualization can be considered
as a component of personalized learning, on the other
hand, it can be used in place of personalized learning.
Individualized learning permits individualization
grounded on the learner’s unique necessities
(Cavanagh, 2014; Lockspeiser & Kaul, 2016). While
in ubiquitous learning environments, users
completely and freely shape their own trails of
education according to their personal interests,
institutionalized learning follows sets of
prerequisites, formal regulations, and curricula.
In differentiated and individualized instruction,
students are provided with real-time individualized
CSEDU 2022 - 14th International Conference on Computer Supported Education
feedback by an instructional and didactical design that
allows them to undertake some control over their own
One way to enable adaptive learning is to create
a Learner Model and a multi-agents-system that
defines intelligent interactive agents, which can
investigate learner’s traces, estimate numerous
indicators, and suggest the best fitting adaptations for
the individua (Ajroud, Tnazefti-Kerkeni, & Talon,
2021). Nonetheless, an adaptive multimedia system
developed by using empirically determined
thresholds for the adaptation algorithm providing
adaptive support in real-time proved to be successful
in improving transfer for stronger learners, but neither
effective nor harmful for weaker learners (Scheiter,
K., Schubert, C., Schüler, A., et al., 2019). Another
way to create possibilities for adaptive learning is by
adaptive tutoring systems that modify according to
the learning styles of the users, students, or tutors,
based on the Felder Silverman Model (e.g., Boussaha
& Drissi, 2021). Other authors have reported benefits
of adaptive e-learning systems based on users’
personal information such as gender, age, educational
level, and background data, learning styles, and
preferences to avoid the ‘one-size-fits-all’ teaching
approach (e.g., Al-Azawei & Badii, 2014). A review
of the existing Adaptive Learning Systems for the
Formation of Individual Educational Trajectory
considers several criteria for ratings such as: area of
application, type of adaptation, functional
persistence, integration within an existing LMS,
utilization of contemporary technologies of
generation, and discernment of natural language and
courseware characteristics (Osadcha, Osadchyi,
Semerikov, Chemerys, & Chorna, 2020).
However, evidence-based research remains
insufficient, as adaptive learning appears to be an
evolving research field (Liu, McKelroy, Corliss, and
Carrigan, 2017). Furthermore, there is a need for
research studies that indicate appropriate
combinations of different types of media and their
influence on shapes and lengths of ILP. Our aim is not
to develop an intelligent tutoring system or an
adaptive educational hypermedia system, through
algorithms (e.g., Vanitha and Krishnan, 2019) or
neural networks (e.g., Saito and Watanobe, 2020). It
is rather to create and research adaptive micro-content
and adaptive micro-activities that can facilitate
competence growth for individual learners using an
existing LMS according to learning theories. Moodle,
as a relatively widely spread LMS at higher education
institutions is suitable for such development and
Adaptive e-learning is associated with robust
pedagogical affordances because it fosters
multifaceted student-centred approaches. Adaptive
presentation techniques to enhance learning
outcomes in higher education related to web-based
learning environments have already been discussed in
the literature, e.g., by Elmabaredy, Elkholy, & Tolba
(2020). Further, Towle & Halm (2005) have
discussed three adaptive strategies related to
synchronous vs. asynchronous learning, rule-example
vs. example-rule, and Feedback adaptation and
concluded that some of the adaptive strategies proved
to be insufficient when being implemented with
students. Out of these three adaptive strategies, we
focus on developing and implementing feedback
adaptation for enabling ILP aiming to embrace the
necessities of all students including low-achieving
students or those with a lower content-knowledge.
3.1 Adaptive Feedback
Appropriate and timely feedback is important for
students towards competence gain and growth. It
supports learners to operate and monitor their own
learning process and to self-control individual
educational decisions (Gutl, Lankmayr, Weinhofer,
& Hofler, 2011). Beside different sources of feedback
such as AI-generated feedback, instructor’s feedback,
or peer feedback, there are also a variety of types of
feedback. While direct, authentic, and individualized
feedback from an instructor is valuable but
considerably time-consuming, tailored feedback can
also be provided by an automated feedback system.
Thus, while manual feedback, given by the instructor,
is usually delayed and might have imperfect timing,
automated feedback which is continuously improving
due to advances in machine learning and natural
language processing, is provided in real-time. What
type of feedback is the most efficient in supporting
the development of appropriate students’ trajectories
in length and durations? Some authors suggest that
feedback plays a significant role as an integrative part
of an adaptive system and emotions and personality
should be considered for its construction (Fatahi,
2019). Rather than choosing one exact type of
feedback, we argue that a proper combination of
several types of feedback, for example behavioural
and cognitive feedback can be the most beneficial.
Cognitive feedback is corrective, epistemic, and
suggestive and supports self-regulated learning.
Corrective and epistemic feedback relate to
Individualizing Learning Pathways with Adaptive Learning Strategies: Design, Implementation and Scale
descriptive learning analytics, whereas suggestive
feedback relates to prescriptive analytics. Behavioral
feedback should be instantaneous, automated, and
equally valuable for monitoring, preparation, and
adjustments (Alasalmi, 2021, p. 136). Further,
generic feedback is context-independent and
contextualized feedback is dependent on the context.
While the general/overall feedback is displayed
immediately with task fulfilment and is independent
of the given solution, the specific feedback is
dependent on the 'correctness' of the given answer.
Therefore, the general feedback aims to provide hints
or links that could lead to further information for
clarification if the task/question has not been
understood well enough. We elaborate these
distinctions with examples in the context of our
sample course in section 4 of this paper. The literature
further differentiates between self-referenced and
reward-based feedback (Maier, 2021), or separation
depending on the complexity of the feedback. So,
feedback can be simple and detailed (elaborated)
(Makhlouf & Mine, 2021). Complex feedback
provides guidance towards the solution, whereas
simple feedback affords short facts about the
accuracy of the result (Belicová, Lacsný, & Teleki,
2018), etc.
3.2 Task Design for ILP Microlearning
The current intensified consumption of Moodle-
based quizzes may lead to an enormous quantity of
produced asynchronous activities and a hyper-
production of tasks. Automatically generated tasks
items are auspicious and comparable to those
generated by humans (Gutl, Lankmayr, Weinhofer, &
Hofler, 2011). Automated processes to generate
content-specific test items are useful for educational
measurement (Gierl & Lai, 2013). On the one hand,
macros and scripts allow for the automated generation
of a vast number of tasks which is beneficial for
otherwise extensive and time-consuming
engagements of instructors. On the other hand, there
is a threat that the manufactured tasks are fragile to
support the gain and growth of specific subject-
related competencies in their completeness. It further
appears that this trend will continue to keep hectic in
the circles of educators, researchers, and designers
because the need for such offers for learning will
continue to grow. To respond to this need, the next
question that deserves attention is how to secure the
quality of the task designs. The quality of task designs
here does not refer only to the types of the tasks,
whether they are single or multiple-choice tasks,
textual or numerical tasks, open questions or to the
linguistic complexity of the items but brings up the
curricular purposes of the task designs and their
didactical potentials into the focus (Donevska-
Todorova, Trgalová, Schreiber, & Rojano, 2021).
Attempts and standards for generating quality task
items aligned to the Common Core State Standards
already exist for example in K-12 mathematics
education (e.g., by Gierl, Lai, Hogan, & Matovinovic,
2015). This paper presents adaptive strategies for
individualized learning through quality designs that
value curricular goals at university education.
3.3 Requirements to Quality Task
Design for ILPs on a Micro Level
Limitations of some types of tasks, e.g., MCQs have
been identified from a pedagogical standpoint in the
literature. Nevertheless, when they are implemented
within a framework including a set of feedback
principles, they support self-regulated learning
(Nicol, 2007). This is also evident when MCQs are
authored and evaluated by students (Bottomley &
Denny, 2011) because collaborative peer activities in
the LMS contribute to individual progress in learning
(Donevska-Todorova and Turgut, 2022).
Other types of tasks require complex
mathematical formulas and symbolic language for
their design and usage. For such tasks, Maxima-based
STACK Assessment tools are applied.
Further in this sub-section, we compare tasks for
purposes of e-assessment and digital non-assessment
tasks (Figure 1). Identified differences aim at the
design of new tasks or interventions in the design of
existing tasks for quizzes in LMS, e.g., Moodle.
Figure 1: Comparison of tasks for e-assessment and digital
non-assessment task designs in e-learning environments
with LMS.
Assessment tasks (Figure 1, left) consist of a task
body and a solution. Any other entry different than
CSEDU 2022 - 14th International Conference on Computer Supported Education
the solution is a distractor. In closed types of tasks,
e.g., SCQs, MCQs, drag-and-drop and fill-in-the-
blanks, the number of distractors is finite and defined
by the task designer. In an assessment situation,
usually only one attempt for submitting the solution
is allowed. In comparison to this type of tasks, digital
non-assessment tasks have more constituents and are
more complex for creation (Figure 1, right). They
have distinctive characteristics and a broader
spectrum of aims: to support exercises and training
skills, development of problem-solving strategies,
advances of other competencies, and so forth.
Therefore, by this type of tasks, it is interesting to
consider is what learning opportunities can be
designed between these two stages: undertaking a
particular task and receiving an automated correct
solution. This gap is of particular importance when
the given student’s solution is not the correct one and
we tackle this issue. We argue that it is worth
allowing multiple attempts to the students for solving
the task. Moreover, it is valuable to invest time and
effort in creating hints as parts of the task design that
can support students along their individualized
learning process. They have the potential to sustain
students’ motivation and prevent early dropouts. It is
of particular importance that these hints should be
appropriate, specific, and content-related.
Aiming at supporting self-directed learning,
besides accessibility options that permit learning
anytime and anywhere, alignment to the curriculum
and accuracy of the content, the designs should meet
the following requirements:
(1) provide task items for answers/ solutions and
distractors (where applicable and as shown in Figure
1) that contribute to learners’ competencies growth
according to a competence model and curricular
(2) afford overall and specific feedback of diverse
types (as discussed in sub-section 3.1),
(3) pose user-friendly displays for easy navigation
(provided by the Moodle interface unique to the
university, e.g., toolbar menus, colour, etc.),
(4) are scalable and empower sustainability
(discussed below in sub-section 5.2).
Some LMS, e.g., Moodle, have embedded options
regarding the first requirement, where competency
frameworks can initially be established on a global
level by administrators and then linked with lesson
plans and activities in one or more courses by
instructors. Such approach allows students receive
reports about their competency growth across a span
of modules along their study.
The third requirement is related to the user
experiences and the potential of the digital learning
environment in supporting affective aspects of
learning as motivation, prevention of boredom, or
3.4 Design of Task Sequences for ILP
Once a non-assessment task (as shown in Figure 1)
fulfills the above quality requirements, it may be
considered for implementation in a task sequence
(Figure 2) that aims to support individual training or
Figure 2: Adaptive task sequencing within a self-regulated
activity, e.g., Quiz in LMS with automated double
The appearance of each of the tasks in the
sequence is randomized in every new trial with a
default option provided by the LMS. Additional
randomization appears within a task, e.g., in “fill in
the blanks” tasks, multiple true/false questions, MCQ
questions, etc. through randomization of the
distractors. This double randomization allows altered
appearances of the same subject-specific content
through automated combinations (Ramos De Melo,
et. al., 2014). The number of attempts per task
sequence is in our case set to unlimited, because they
aim to support learning and not assessment.
Advanced and evaluated task sequences, that are
developed, tested, and evaluated through the
iterations in a complete DR cycle (Psillos &
Kariotoglou, 2015) may be offered to the students for
Drawing on the theoretical background that considers
adaptive and individual microlearning presented in
the second section and the literature discussing the
current state of research about adaptive strategies in
the third section, this paper considers the following
research question:
RQ: How can adaptive strategies as feedback
adaptations, task design, and task sequence design in
the LMS Moodle affect individualization of
microlearning pathways of undergraduate students?
The complexity of the research question in view
of subject-specific, pedagogical, and technical
aspects requires a methodological approach with an
iterative nature that can secure development,
Individualizing Learning Pathways with Adaptive Learning Strategies: Design, Implementation and Scale
implementation, and evaluation elements. Therefore,
this work is based on the principles of Design
Research (DR) (Kelly, Lesh, & Baek, 2008)
involving mixed methods and this paper explains a
pre-study that is the first of a total of seven phases of
a complete DR initial cycle. The pre-study is followed
by a pilot study in the second phase, and it is also
briefly outlined in the upcoming sub-section.
4.1 Data Collection and Data Analysis
In phase one of the complete DR cycle, a pre-study
took place in the first half of the winter semester
2021/22 at the University of Applied Sciences HTW
Berlin in Germany.
Besides on findings from a literature review and
theoretical grounding, the pre-study relies on two
sources for data collection: an internal system for
teaching, learning, and research LSF at the university
and the LMS Moodle. Four out of the eleven courses
in the LSF data pool were selected for the analysis
(Table 1). In addition, a Moodle course was
established for design and trials of new activities and
question banks.
Table 1: Data Collection in the Pre-study: Courses for
Investment and Financing in the winter semester 2021/22 in
the university LSF and the LMS Moodle.
1 Number of courses in LSF 11
2 Selected Moodle courses for
3 Additional Moodle course for the aims of
the pre-stud
4 Question bank with categories and labels
according to competencies, task types and
levels of difficult
5 Generated task queries (task text, task
solution and destructors)
6 Generated item responses for various types
of feedback adaptations
All generated items are categorized and labelled
according to three criteria: subject-specific
competences, type of the task and level of difficulty
of the task. This categorization enables easier
structuring of the question bank and randomization of
the tasks in the task sequences.
In the second phase of the complete DR cycle, a
pilot experimental trial is planned for the second half
of the winter semester 2021/22 at the University of
Applied Sciences HTW Berlin in the frame of one
module. The data collection and data analysis in this
phase are two-step processes having two goals.
The first set of data provided on a basis of a
questionnaire for the university educators serves for
the creation of a competence model related to a
revised Bloom Taxonomy specifically developed for
the module. Further, based on the answers given by
the participants and the competence model, Moodle-
based task items and activities for microlearning can
be (re)designed.
The second set of data will be collected via the
LMS Moodle-Course. This set of data aims to provide
reactions and commentaries about the quality of the
prototype of the design including the feedback
adaptations, that was described in the previous two
sections of this paper.
The process of (re)creating and iterative experimental
testing of the tasks and activities will undergo the
other five phases of the DR cycle.
Learning possibilities in the LMS Moodle at our
university are grouped as resources and activities.
Likewise, in the processes of design and
implementation, we distinguish between these two
groups of learning opportunities. Here, we ‘zoom in’
a single task design, with embedded feedback
alternations enabling individualization of learning
trajectories (Figure 3), for a Moodle quiz activity.
Figure 3: Feedback adaptations and micro-level sequencing
in hypothetical individual learning pathways (ILP).
Instead of presenting the development of the
various ILPs in a typical algorithmic if-then loop and
cyclic manner, the visualization in Figure 3 offers an
outline of the effects of the feedbacks on the length
and the shape of the individual learning trajectories.
Hence, each micro-ILP beginning with a task body
(notated with B in Figure 3, called question text in
Moodle) directly ends with a direct correct solution
(S in Figure 3) immediately accompanied with both
behavioural and overall feedback (GF in Figure 3) or
continues with a distractor (Di, i=1,2,…,n in Figure
3) supplemented with a specific cognitive feedback
Ti, i=1,2,…,n in Figure 3). Thus, the shortest length
of a possible ILP is three steps: B-S-GF, and the
longest individual path depends on n, where n is the
maximal number of allowed trials, which is the same
as the number of feedbacks embedded in the task
CSEDU 2022 - 14th International Conference on Computer Supported Education
design, and the student’s choices. In our design, n is
set to 3. So, the student is allowed to undertake the
same task in a single task sequence for a up to three
times and each time he/she enters a wrong answer Di,
an immediate feedback Ti of altered sort as described
in sub-section 3.1 follows. Meanwhile, every new
entry following feedback decreases the full score of
total points for the task by one third. This fosters the
student to make decisions about the distinct further
steps that they prefer to make. In this way, the student
is triggered to take responsibility about own decision
making which fosters self-regulation of learning
processes and as a result thereof, a development of
personal competencies, besides content-specific
competencies and knowledge growth. This is in line
with recommendations that “technology must not
take away control from the learner, but instead
provide stimuli to increase competencies for self-
directed learning” (Gutl, Lankmayr, Weinhofer, &
Hofler, 2011, p. 323). This suggests a didactical
adaptation of the individual's profile in the LMS as a
compulsion strengthening differentiation of the ILPs.
In the next section, we proceed by presenting mall
well-planned portions of content for cognitive
activation and motivational continuity during
engagement with those units created with the LMS
Moodle at our university.
5.1 Contextual Examples of Task
Designs and the Feedback
Adaptation for ILP
To illustrate the task designs including feedback
adaptations for supporting ILP, we provide three
contextualized examples in Financial Mathematics
and investment decisions created with the LMS
Moodle at our university.
The question bank of the created tasks includes
single choice, multiple-choice, fill in the blanks, and
open-ended textual and numerical questions. The
sample can easily be accustomed to supporting
numerous variations of the tasks and the feedback.
The question bank is structured, and the tasks are
categorized and labelled according to curricular
competencies for the module and as described in sub-
section 4.1.
In sub-section 3.3 (Figure 1) we explained that the
attention is on the ‘hidden’ activities and adaptive
feedbacks that happen when an improper answer of a
non-assessment task is given by the student. To
exemplify this, the first example showcases a task
design (Figure 4. a)) with an open short numerical
answer and immediate general behavioural feedback
(as discussed in sub-section 3.1) appearing with an
incorrect solution. The feedback is shown in the
orange rectangle in Figure 4. a). Below it, in Figure 4.
b) there is cognitive feedback containing a
mathematical formula inserted in Moodle with Latex
as a hint. An interval for a tolerated mistake in the
rounding is also restricted and defined in the Moodle
task. An interactive combination of feedback and
hints provides meaningful helpful information and
guidance for the students. The correct solution
appears only in the final attempt, so when n=3
according to the description of the ILP on Figure 3.
Figure 4: a) Preview of the immediate general behavioural
feedback in an open numeric task design appearing with an
incorrect solution. b) Preview of cognitive feedback
containing a mathematical formula with Latex.
Individualizing Learning Pathways with Adaptive Learning Strategies: Design, Implementation and Scale
The second example (Figure 5. a), b) c), and d))
displays a “drag and drop into text” task with six
choices, combined feedback, and multiple trials. The
combined feedback consists of cognitive corrective
and cognitive epistemic feedback. It illustrates a
possible ILP in which a correct solution and overall
feedback are accomplished in a second attempt. So,
the ILP looks like: B - D1/F1 - D2/F2 S/GF in
relation to the micro-level sequencing shown in
Figure 5: Preview of a ILP: B - D1/F1 - D2/F2 – S/GF.
Figure 3 in the previous section and the research
question about the effects of the feedback adaptations
of the formation and length of a ILP (in section 4).
The third example demonstrates a design for a
True/Falls task (Figure 6) and two types of feedback:
cognitive epistemic feedback (above on the right with
yellow colour on the figure) and cognitive suggestive
feedback (below with orange colour on the figure), as
discussed in sub-section 3.1. Based on the feedback,
the student can decide in which direction can his/her
individual path continue. By following the epistemic
feedback, which is formulated as a question, the
student is tempted to make a decision based on
reflection on own knowledge and reconsideration or
consolidation. If the student succeeds in recalling
knowledge, which is a subject-specific competence
(defined in the competence model mentioned in
section 4), the student can move forward in the ILP.
Alternatively, the suggestive feedback guides the
student to read again or repeat already familiar basic
concepts, thus, to redo some of the previous steps in
the ILP. So, it suggests a redirection to a resource
instead of continuation with a new trial for the task or
new task in the sequence. In this way, these two
feedback items deliver different proposals for further
adequate activities regarding the type and the
complexity degree, refine and shape the ILP, and
determine its length in the microlearning process,
which is related to the posed RQ about the influence
of the task design and the feedback adaptations on the
student’s ILP stated in section 4.
Figure 6: Preview of specific cognitive epistemic and
suggestive feedbacks in a single solution true/false task.
A look back on these three tasks with accompanying
adaptive feedback, enables us to briefly evaluate them
whether they meet the requirements of quality task
design for ILP on a micro-level discussed in sub-
section 3.3. Each of the tasks provides task items for
CSEDU 2022 - 14th International Conference on Computer Supported Education
answers/ solutions and distractors and afford overall
and specific feedback of diverse types. Therefore,
they fulfil the first two criteria. The visual appearance
of the tasks’ items, the appropriate feedbacks, their
arrangement, and the colour is university-unique,
which fulfils the third criterion. The fourth criterion
for the quality of the task design is related to
scalability of the tasks and is discussed in the next
Let us now summarize the above discussion with
regards to the research question. The adaptive
strategies effect the ILP in the following ways:
The adaptive feedback acts as a turning point in
decision making and with that shapes the ILP.
The adaptive feedback influences the number
of steps that individual learners make and with
that optimizes not only the length of the ILP,
but also stimulates the duration and the
continuity of the engagement.
Besides the standard task text/body, a quality
task design involving precise distractors and
embedded feedback (which is not the case in
assessment tasks) can support training and
contribute to deeper understanding along an
Additionally, to the randomization possibilities
provided by the LMS, task sequences are adjustable
and allow students’ decision-making and assuming
personal responsibility for their ILP.
5.2 Further Considerations for
Re-design, Evaluation, and Scale
In the previous sub-section, we have discussed
possible designs and exemplified contextual
implementation of micro-content and micro-activities
that enable individual knowledge building and exploit
benefits of adaptive micro-learning in higher
education settings in line with the approach suggested
by Gherman, Turcu, C.E., & Turcu C.O. (2021). Yet,
personalized, and adaptive learning, are not without
limitations (Tan, Soler, Pivot, Zhang, Wang, 2020).
We contemplate that a two-part process for reviewing
and evaluating (Gierl & Lai, 2016) of the designed
tasks and feedback adaptations is vital for their high
quality and it will be undertaken during a later phase
in the DR cycle. Further, transferability of the applied
adaptive strategies in other courses is also not
straightforward. Some issues of item generation and
scale with regards to sustainability are mentioned by
Soares, Lopes, & Nunes (2019). We currently
consider two ways for scale: through competency
frameworks, either on an administrative or on a
course level and through automatization with pivot
tables and additional modifications. With the
competency frameworks, students achivements can
be digitaly traced and reported towards an outcome-
based education across many courses on the long term
during the whole study programm which is valueable
for their furture carriers. Further on, we point out that
these results can be extrapolated beyond educational
coursework because these aspects ay cross−apply to
professional working environments. Lastly, novel
mobile touch devices, such as smart phones, may
require re-design of the adaptive strategies or
technical interventions in the LMS, which is in line
with (Papadakis, Kalogiannakis, Sifaki, & Vidakis,
The availability of subject-specific content structured
in a usual weekly manner in the LMS is no longer
equally effective for the students and does not
correspond to their necessities. This paper
emphasizes the importance of shifting the educational
focus from content delivery towards didactically solid
adaptive design of micro-content and micro-activities
in innovative tertiary education (discussed in section
3). Individualization of learning pathways is
didactically made possible using adaptive learning
strategies, like feedback adaptations, task design and
task sequence design that were technically
implemented through the intelligent features of the
LMS Moodle for modern education delivery and
illustrated with contextual examples (in section 5).
The proposed fine-grained learning activities were
designed in a pre-study based on literature review and
learning theory about competencies at higher
education. The further (re)design, testing, and
evaluation will undergo a complete DR cycle
including iterative design experiments (methodology
presented in section 4). Challenges of detection,
recognition, and support of all realistic multiplicities
of individuals’ learning styles, mutable learning
progress, and contextually dependent learning
accessibility open avenues for further research.
The research work presented in this paper is
undertaken at the University of Applied Sciences,
Hochschule für Technik und Wirtschaft Berlin,
Individualizing Learning Pathways with Adaptive Learning Strategies: Design, Implementation and Scale
Germany in the frame of the project” Curriculum
Innovation Hub” granted by Stiftung Innovation in
der Hochschullehre.
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