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A predictive model for the identification of learning styles in MOOC environments

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Abstract

Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners. In parallel, recent advancements in machine learning techniques and big data analysis have created new opportunities for a better understanding of how learners behave and learn in environments known for their massiveness and openness. The work is about predicting learners’ learning styles based on their learning traces. The Felder Silverman learning style model (FSLSM) is adopted since it is one of the most commonly used models in technology-enhanced learning. In order to attend our objective, we analyzed data collected from the edX course “statistical learning” (session Winter 2015 and Winter 2016), administered via Stanford’s Logunita platform. The results show that decision tree performs best for all 3 dimensions, with an accuracy of higher than 98% and a reduced risk of overfitting the training data.

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Acknowledgements

The authors are grateful to CAROL (the center for advanced research through online learning), university of Stanford, for providing the Dataset necessary for accomplishing this research.

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Authors and Affiliations

  1. IRF-SIC Laboratory Ibn Zohr University Agadir Morocco, Agadir, Morocco

    Brahim Hmedna, Ali El Mezouary & Omar Baz

Authors
  1. Brahim Hmedna
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  2. Ali El Mezouary
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  3. Omar Baz
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Correspondence to Brahim Hmedna.

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Appendices

Appendix 1: Comparison of features related to some data-driven study

 

LS

Features

Study

[28]

[40]

[38]

[39]

Processing dimension

Active

Selfass_visit

   

*

Self_twice_wrong

   

*

Exercise_visit

*

 

*

*

Exercise_stay

*

 

*

*

Forum_post

*

*

*

*

Nbr of discourse interactions

 

*

  

Right answer after seeing a movie

 

*

  

Score for practical questions

 

*

  

Write_msg_chat

*

 

*

 

Use_mail

  

*

 

Reflective

Content_visit

   

*

Content_stay

   

*

Outline_stay

   

*

Example_stay

*

 

*

*

Selfass_stay

*

 

*

*

Quiz_stay_results

  

*

*

Forum_visit

  

*

*

Nbr of discourse interactions

 

*

  

Nbr of questions asked

 

*

  

Score for theoretical questions

 

*

  

Listens_chat

  

*

 

Input dimension

Visual

Ques_graphics

   

*

Right answer after seeing an image

 

*

  

Right answer after seeing a movie

 

*

  

Verbal

Content_visit

   

*

Ques_text

   

*

Forum_visit

   

*

Forum_stay

 

*

 

*

Forum_post

   

*

Nbr of discourse interactions

 

*

 

*

Nbr of questions asked

 

*

  

Right answer after seeing a movie

 

*

  

Understanding dimension

Sequential

Ques_detail

   

*

Guided to solve a problem

 

*

  

Navigation_step_by_step

*

 

*

 

Exam_res_high

  

*

 

Global

Outline_visit

   

*

Outline_stay

   

*

Ques_overview

   

*

Ques_interpret

   

*

Ques_develop

   

*

Navigation_skip

*

 

*

*

Navigation_overview_visit

   

*

Navigation_overview_stay

   

*

Solve a problem straight away

 

*

  

Appendix 2: Elbow curves

See Fig. 14.

Fig. 14
figure 14

Elbow curves plotted from the clustering results of the “Winter 2016” session: a active; b reflective; c visual; d verbal; e sequential; and f global learning styles

Full size image

Appendix 3: Clustering results

See Tables 12, 13, 14, 15, 16 and 17.

Table 12 Active learning style
Full size table
Table 13 Reflective learning style
Full size table
Table 14 Visual learning style
Full size table
Table 15 Verbal learning style
Full size table
Table 16 Sequential learning style
Full size table
Table 17 Global learning style
Full size table

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Hmedna, B., El Mezouary, A. & Baz, O. A predictive model for the identification of learning styles in MOOC environments. Cluster Comput 23, 1303–1328 (2020). https://doi.org/10.1007/s10586-019-02992-4

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