Analysis of Discussion Forums of a Programming MOOC

Abstract

The work presented in this article focuses on the description of the learning process in the MOOC of algorithms and programming. It focuses more specifically on the study of the learning strategies adopted by students by examining discussion forums in a MOOC of algorithms and programming designed for undergraduate students. The results stated that students used two learning strategies to construct algorithmic content: cognitive strategies and social strategies. These results improve the didactic research on the MOOC by providing researchers with elements for the study of learning process in MOOCs.

A Didactic Study of an Algorithmic and Programming MOOC: Learning Strategies Adopted by Students and their Difficulties

“…This traditional model of teaching at universities has proved its inadequacy. Thus, we observe weak supervision of students, a pedagogy not adapted to the student's rhythm, a high dropout rate, a training not adapted to the job market (low employability) but also not accessible to the greatest number of students [1].…”

“…Other studies analysed the forum discussions of programming MOOC [1], [20]. The study of Babori [1] described the learning strategies (cognitive and social strategies) adopted by students by examining discussion forums in a MOOC of algorithms and programming.…”

“…Other studies analysed the forum discussions of programming MOOC [1], [20]. The study of Babori [1] described the learning strategies (cognitive and social strategies) adopted by students by examining discussion forums in a MOOC of algorithms and programming. More particularly, Nelimarkka et al [21] examined the social help-seeking strategies in a programming MOOC: seeking help from friends and seeking help from alumni and teacher communities.…”

How do Chinese students effectively convey emotions through the discussion forums in the LMOOCs?

“…As far as we are concerned, multi-level sentiment analysis can help detect vague semantic links and complicated sentiment information (Ha et al, 2019), hence it is of great significance for comment analysis. Besides, as forum comments abound in extremely short comments with ineffective or irrelevant information (Babori, 2021), further document-level analysis may help cleanse the data noise and enhance its reliability. Therefore, we maintain that more comprehensive further studies can be carried out to detect the subtly different sentiments at different levels.…”

Research on the predictive model based on the depth of problem-solving discussion in MOOC forum

“…For example, Garrison et al ( 1999 ) developed coding framework for learning reflection in online discussion. Babori ( 2021 ) focused on exploring learning strategies in MOOCs. Hew and Cheung ( 2008 ) compared different types of questions in discussion forum and found that questions about viewpoints were regarded as a more useful technique for encouraging participation than questions for clarification.…”