Adaptive systems based on thinking and learning styles.

The idea of adaptive learning goes back to the work of B.F. Skinner in the 1950’s. Adaptive learning adjusts the learning experience based on a student’s progress. Basically it shifts from the teacher-centric model to a student-centric model. Two important factors that an educator has to take into account in order to be able to adapt the educational content to each individual learner are their thinking and learning styles. Moreover, one has to also keep in mind the curriculum requirements. The thinking style refers to the unique way each person thinks and behaves. These thinking styles are the outcome of experiences and interaction we have had with the people around us. Moreover, the learning style refers to the method or strategy that every person has when learning.

An experiment using an Adaptive e-learning hypermedia system based on thinking and learning styles (AEHS-TLS) was designed to explore the effect of adaptation to different thinking and learning styles. This experiment was conducted at the Annaba University in Algeria with 40 students for 4 different subjects (ORL, dermatology, ophthalmology and language). This system was organized into three components:

  1. The domain model is used for organizing the learning content.
  2. The learner model is the representation of information about an individual learner. The kind of adaptation which the system has to deliver depends on the nature of this information. The information about the learner consists of the goals and preferences, the thinking and learning style, and the knowledge and performance.
  3. The adaptation model specifies the way in which the presentation of the content is altered according to the knowledge and thinking style of the learner. The adaptation model is made up of three sub models which are the narrative paths sub model that supports a narrative graph, which contains a different path for each thinking style, the pedagogical rules sub model which consists of a set of rules and activities that controls the learning process and the control sub model which selects the most suitable path to deliver pages with learning content to students.

The hypothesis of this adaptive system was proved that it can increase the level of learners. What is even more interesting about this system is that it does not only adapt the teaching material according to the thinking and learning styles, but it also takes into account the learner’s goals and preferences.

For future work, an area in adaptive systems which requires improvements is the ability to create adaptive tests. By this, educators will have the opportunity to both adapt the content of delivery and the evaluation of the learner.
  • Mahnane,L , Laskri, M.T and Trigano,P. (2013, May) A model of Adaptive e-learning Hypermedia System based on Thinking and Learning Styles. International Journal of Multimedia and Ubiquitous Engineering, 8(3), 339-350.

A Social Personalized Adaptive E-Learning Environment: A Case Study in Topolor

What is an Adaptive e-Learning environment? This is a system which provides content to the learners in an adaptive manner. This means that new content is only provided based on the following characteristics including the learner’s preferences, needs and knowledge. Social e-Learning (and adaptive) environments have emerged which provide the users with features similar to those found on social networking websites. These consist of sharing, rating and commenting etc.

The aim of this research is to find out whether a system with a number or features typically found on social websites increase how useful and useable a system is. Research indicates that when students are introduced to a new system, a lot of time is used up when the students explore this new system. While this is not necessarily bad, the students would not need to waste this time if they are introduced to a system which they are already familiar with.

Topolor is intended to be an environment which the students are already familiar with. It includes the following features: a home page where the user can see any posts which were created by other students, view any asked questions and post a new status. A “Module Center” will allow the users to access all modules which are available online and also provides various recommendations. A “Quiz Service” runs automatic quizzes which consist of a number of random questions and once this quiz is over feedback is sent immediately to the learner.

An experiment which consisted of twenty-one students was set up were the students during a lesson on “Collaborative Filtering” were using Topolor while following a list of items which they needed to do. The tasks ranged from create a Learning Status to sending messages to answering peer questions. Ten participants then answered a questionnaire about Topolor. This consisted of two sets of eighteen questions each, a section about the ease of use and the other about the usefulness of the features found in the system.

When the mean of the results was calculated, the final data points out that most of the students classified the features easy to use. The results also show that on the whole the students found all the features as being useful. A small amount of qualitative data was also collected and the students generally gave positive views noting that a good feature is its similarity to social networking sites.

Some future updates are also planned. These include having more features such as favourite-ing other user’s statuses, an improved version of the messaging system where notifications are better presented to the user and the ability to share statuses.


Shi, Lei, Cristea, Alexandra I., Foss, Jonathan G. K., Al Qudah, Dana and Qaffas, Alaa. (2013) A social personalized adaptive E-Learning environment: a case study in Topolor. IADIS International Journal on WWW/Internet. pp. 1-17. ISSN 1645-7641 (In Press)


Activity Theory, Adaptive System and Responsive Design

Learning from online sources has become a very common thing. (What is eLearning?) Developments are continuously being made to create the ideal situation for the learner to learn according to his/her own individual needs. With this aim in mind, adaptive e-learning methodologies were also introduced.

Paramythis and Loidl-Reisinger refer to it as the process of monitoring your students doing activities; interpret such activities on “domain-specific models”, define their requirements after doing deep interpretations and act upon them to produce a learning environment which better caters for the need of the student. Adaptions in an e-learning environment starting from adaptions in the system interface itselfshould be various. Furthermore, changes can also be made in the structure of the coursethus making it personalized for the learner. Adaptations can also be made in the methods presented to the students where they discover new content from various online resources. Finally, amendments in the learning process might also be required one of which being the communication between the teacher and the student (Paramythis & Loidl-Reisinger, 2004) .

Adapting to the students’ needs should also be made in terms of technology and devices. Nowadays, students’ e-learning does not necessarily take place on a desktop computer. Considering the drastic reduction inthe prices of the latest technology tools, I believe that most students also have access to smart phones, tablets and other devices. Hence it is fundamental that the online material provided to them is responsive according to the device’s screen. This is because structures which are suitable for a desktop computer does not necessarily suit a smart phone. This is known as responsive design. (Evans, 2015)

An adaptive e-learning environment is mostly based on models, one of which is the student. In the article “Student Modelling in Adaptive E-Learning Systems” one can identify how these models are incorporated within the process. Adaptive learning should be based not just on the students prior knowledge but also on his/her behaviour, hence the linkage of online structures should be based on the students profile including what his likes/dislikes are. It should be based on the principle that the learning characteristics of each individual is different and that hence different educational settings are needed. Content should be automatically adapted to satisfy the needs of such students’ models.

“The aim of adaptive e-Learning is to provide appropriate information to the right student at the right time”
(Esichaikul, Lamnoi, & Bechter, 2011)

Such models, activity theory and adaptive e-learning systems were also discussed in Pena-Ayala, Sossa and Mendez article. The Activity theory framework knows its root to Aleksei Leontiev a Russian psychologist and Kaptelinin describes it as:

“Purposeful, transformative, and developing interaction between actors (“subjects”) and the world (“objects”).”

This theory has been considered ever since the 1990’s as a fundamental mark for Human-Computer-Interaction (Kaptelinin). It’s aim is that of comprehending the mental abilities of different individuals while learning and this is why in their article Pena-Ayala, Sossa and Mendez carried out a case study to identify how this theory could help in building adaptive e-learning systems with the final aim of enhancing the students’ apprenticeship through previous predictions of the learning result.

The activity theory is based on several principles including: object-orientedness, hierarchical structure, mediation, internalization-externalization, anticipation and development. Furthermore, to attain different AT goals, 4 architectures have been developed being Activity as:

  • Basic Unit
  • Individual Level
  • Collective Level
  • Network Level

The case study was a national one and from all the electronic applications, 200 subjects were chosen. From these 200 only the first 18 who managed to finish all pre-stimulus (including exercisesand questions) in time and correctly were encouraged to continue to the next phase. During this process information gathering about the students was being made so that different students’ profiles were eventually developed. These 18 students were then divided in two groups, 9 of which being in a controlled group and the others being in an experimental group. Throughout this process, students were continuously being tested and monitored and their domain knowledge was also discovered. What was concluded was that the lectures which implemented the most effective principle for apprenticeship and anticipation, attained a higher level of students learning when compared to the apprenticeship attained through lectures chosen randomly without incorporating any personalized options such as those provided by an AeLS.

It was concluded that: “the students’ apprenticeship is successfully stimulated when the delivered lecture matches their profile” regardless of the subjects (students) having lower domain knowledge. This is because the results also concluded that given stimuli based on the anticipation principle, the experimental group, despite having lower domain knowledge when compared to the controlled group, whose lectures were chosen randomly, produced higher apprenticeship and learning. With the final conclusion being that activity theory is indeed very convenient for implementing a “student-centered” AeLS. (Pena-Ayala, Sossa, & Mendez, 2013)