DEVELOPMENT AND EVALUATION OF AN OPEN SOURCE ADAPTIVE LEARNING
ENVIRONMENT BASED on LEARNERS’ COGNITIVE STYLES and ENGAGEMENT
Introduction
In this study it’s aimed to discuss findings of a research study which is as a part of a
dissertation titled “Designing Developing and Evaluation of an Adaptive Distance Learning
Environment”. The Environment is being developed by taking dimensions of field dependency
and learner engagement. The dimensions were decided after a literature review and a focus
group study of experts in the fields of distance education, learning psychology, computer
engineering and instructional design.
Adaptive Educational System and Learner Model
An Adaptive Educational System (AES) adapts its key functional characteristics like
content presentation, navigation support or content difficulty according to learner model
(Magnisalis, Demetriadis, & Karakostas, 2011) similar in an one-to-one instruction. It’s
possible to argue that personalization of an online course has improving effects since as Bloom
(1984) expressed; compared to classroom tutoring there is two standard deviations increase
in one-to-one instruction.
The learner model is used to derive personalization based on important learner and
learning characteristics for the learning process (Vandewaetere, Desmet, & Clarebout, 2011).
There are different approaches when defining a learner model. Chrysafiadi and Virvou (2013)
reviewed the literature and found basically seven learner characteristics to model. They are
knowledge, errors/misconceptions, learning styles & preferences, other cognitive aspects,
affective features, motivation and meta-cognitive features. Through a literature reivew and a
focus group study researchers decided to adapt learning processes according to field
dependency cognitive style and learner engagement.
Field Dependency, Learner Engagement and Adaptation Considerations
In this section field-dependency (FD), learner engagement and their implications to
the adaptive system which is being developed will be explained. Field dependecy cognitive
styles may effect learning design in terms of nonlinear learning, learner control, navigation
and instructional strategies (Chen & Macredie, 2002). Chen and Macredie (2002) adapted
from Witkin, Moore, Goodenough, and Cox, 1977; Jonassen and Grabowski, 1993; and
Morgan, 1997 and listed the differences between field dependent and field independent
cognitive differences as follows.
The individuals with field dependent cognitive style find it difficult to restructure new
information and forge links with prior knowledge on the other hand, the field independent
individuals are able to reorganize information to provide a context for prior knowledge. Also
Jia, Zhang, and Li (2014) pointed that field independent (FI) individuals by access working
memory efficently can filter out irrelevant information but FI individuals can’t. FD individuals
are externally, FI individuals are they are internally directed. FD are influenced by salient
features they accept ideas as presented but FI individuals they accept ideas strengthened
through analysis. FD individuals are better in social orientation but FI individuals are
influenced less by social reinforcement. Cognitive styles unlike learning styles are not just
related to learning. Cognitive style deals with the form of cognitive activities like thinking,
perceiving (Triantafillou, Pomportsis, & Demetriadis, 2003) conceptualizing, organizing and
recalling information. That is also may effect individual’s social presence and roles in learning
environments. . FD learners prefer working in groups but FI individuals prefer working alone.
FD individuals experience surroundings in a relatively global fashion but FI individuals are
experience surroundings analytically. FD individuals passively conforming to the influence of
the prevailing field or context they demonstrate fewer proportional reasoning skills on the
other hand FI learners discrete from their backgrounds, they demonstrate greater
proportional reasoning skills.
Another aspect for adaptation is learner engagement. Quantitative observational engagment
measures can be listed as follows (Henrie, Halverson, & Graham, 2015) number of posts to a
discussion board, time on task, attendance, assignment completion, number of on-task or ooftask behaviours, number of edits made during a writing task or discussion board activity,
number of page views in an online resource. Learners’ engagement will be assed using
quantitative observational engagment measures from system logs and popup questions.
Learning content interaction will also be tracked eg. video that is watched or skipped.
Adaptation
Course presentation and navigation will be adapted as follows:
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Simplier interface design and content will be used for FD learners, but FI
learners will be faced more complicated content and interface.
Field dependent learners will be supported a dictionary or hints of related
content. They will be given recall aids. The links to previous contents will be
emphasised.
Field independent learners are asked and supported more to use forums,
discussion boards.
FD learners will be supported by direct learning paths, but FI leraernes will be
free to set their learning navigation.
Deep approach will be used for FI learners on the other hand surface
approach will be used for FD individuals.
Adapted e-mails will be sent to the low engaged learners.
Time spend on the system, correct and wrong answer ratio will be assesed
and appropriate feedback will be produced by the system.
Adaptive Learning Environment Design
Open source software is being used to develop the adaptive learning environment.
The structure is built on open source conent management system (CMS) Drupal. Assesment
will be held through Drupal modules Quiz and H5P. Learning analytics will be collected using
PIWIK, open source site analytics software and also learning activities will be tracked using
LearningLocker which is a open source learning repository (LRS) store software.
During the spring semester a MOOC will be published using the developed adaptive
learning environment. System’s adaptation success and its effect on learners’ will be
evaluated. To evaluate these variables learning analytics, semi structured interviews and
system logs will be used. Findings will be discussed with the experts.
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