Exploiting OLM and Learning Analytics Visualization with respect to Students Learning Styles and the Affective States

It is easy for a teacher to identify the learning preferences and behavior of students in a classroom environment. ?On the other hand, in web-based learning environments such as Learning Management Systems (LMSs), the lack of students? physical presence make it challenging to identify their learning

Project Title

Exploiting OLM and Learning Analytics Visualization with respect to Students Learning Styles and the Affective States

Project Area of Specialization

Artificial Intelligence

Project Summary

It is easy for a teacher to identify the learning preferences and behavior of students in a classroom environment. ­On the other hand, in web-based learning environments such as Learning Management Systems (LMSs), the lack of students’ physical presence make it challenging to identify their learning preferences and behavior automatically.

Recent research about students learning indicate that students learn less, when the learning contents either do not match with their learning preferences such as learning style or  the contents  provided do not take into account students’ behavior, such as affective states. Most of the research with respect to learning styles and affective states in web-based learning environments, conducted so far, do not treat it together for developing a student model. Although they both are important and contribute equally towards students learning. There is a very little research that takes into account both the learning styles and affective states for developing a student model. The modeling approaches adapted by such web based learning systems are usually static as well as provide access only to the teachers or course designers. Furthermore, the reported results lack the visualization features.    

Therefore, the aim of this research project is firstly to develop a module for the dynamic and open learner modelling of students’ learning styles and affective states using visual learning analytics. Secondly to develop a module for the personalized feedback, keeping in view the learning style and affective states of a student.   

The dynamic feature deals with real time reporting of learning styles and affective states, based on the changes in the respective patterns. The open learning model introduces the concept, where not only the teachers, but also the students have access to their own model, The teachers may view the learning styles and affective states of their students, in order to assist them by providing either suitable contents or modify their teaching strategy accordingly. Similarly the students may be able to view their own learning styles and affective states. This may give awareness to each student about their learning styles and affective states.

The personalized feedback module provides students with information about the type contents that will be more appropriate for them to learn keeping in view their learning style and current affective state.       

The identification of learning styles will be based on a model developed by Felder-Silverman (FSLSM). Similarly the identification of affective states relevant to learning will be based on various recent research studies.   

Project Objectives

To develop a web-based system that will:

• Automatically detect Learning Styles and Affective states from students’ learning preferences and behaviour in Learning Management Systems.
• Visualize the various features such as Discussion Forums, Quizzes, Assignments etc. of each individual with respect to learning styles and affective states. .
• Facilitate Course Instructors to know the students' behaviour by taking into account their Affective States and individual Learning Styles and consequently enhance the teaching and learning experience.
• Exploit the OLM (Open Learner Model) and enable the students to view their current Affective State and Learning style, and provide visual and textual information to improve their learning style, and overcome of the current affective state.
• Constantly update pre-existing data (Results) for greater accuracy and reduce error margins.
• Increase the understanding of teachers about how students learn, and to allow students to be self-aware of their strengths and weaknesses.
• Provide personalized feedback interface generated by the system based on the individuals’ profile generated by the system.

Project Implementation Method

The system will be developed using PHP programming language. The User Interface will be designed in Adobe XD and then coded using HTML, SASS and JavaScript. The results are visualized using JavaScript data visualizations Libraries.

Data Extractions and Processing: The data extraction module extracts relevant data of students’ preferences and behaviour from the LMS database, the collected data will be used for calculations of Learning Style against four dimensions of FSLSM. The raw data is used for the calculation of an individual’s Learning Style and the Affective States.

Integration and Analyzing: The system to be developed is integrated with the LMS database, only existing users will be able to access the system, the user credentials will validate from LMS Database. The system collects student’s data from the LMS database and logs. The collected data is being processed by the Affective State and Learning Style Analysing Module.

Visualization: The student preferences and behaviour patterns are visualized on Instructor panel and the data of individual student is available on his panel. The reports of students’ performance will be generated by the system and downloaded on the local storage on request by the administrator.

Personalized Feedback: The student gets the personalized feedback from system to improve their learning skills by using personalized feedback, the system suggest learning content that is best for his learning.

Modules in this application: -

• LMS Integrated Login
• Educational Data Extractor
• Affective State and Learning Style Predictor
• Students preference and Behavioral Patterns Visualizer
• Instructor Panel
• Student Panel
• Personalized Feedback Panel
• Reports Generator

Basic Block Diagram of System:

Benefits of the Project

• Help course instructors to identify and implement effective learning strategies for students in Web-Based Learning environments, particularly in LMSs.
• Analyze the learning styles of individual classes and hence help the teachers organize their lectures and material accordingly.
• Diagnose the weaknesses of an individual students' learning style, and thus providing each individual insight in order to study more effectively.
• Analyze and match teachers and students with compatible teaching and learning styles.
• Enable Instructor to provide learning material and exercises in accordance with the student's preferences to generate an interest in learning.
• Automatically identify learning styles and offer courses that match students' mode of learning

Technical Details of Final Deliverable

The final deliverable is A System for Exploiting OLM and Visual Learning Analytics for Students Learning Styles and Affective States. The system will be a web-based application along with documentation manual having all the technical details of application.

Final Deliverable of the Project

Software System

Type of Industry

IT

Technologies

Artificial Intelligence(AI), Others

Sustainable Development Goals

Quality Education

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com