Abstract
Students’ interest and involvement during class lectures is imperative for grasping concepts and significantly improves academic performance of the students. Direct supervision of lectures by instructors is the main reason behind student attentiveness in class. Still, there is sufficient percentage of students who even under direct supervision tend to lose concentration. Considering the e-learning environment, this problem is aggravated due to absence of any human supervision. This calls for an approach to assess and identify lapses of attention by a student in an e-learning session. This study is carried out to improve student’s involvement in e-learning platforms by using their facial feature to extract mood patterns. Analyzing themoods based on emotional states of a student during an online lecture can provide interesting results which can be readily used to improvethe efficacy of content delivery in an e-learning platform. A survey is carried out among instructors involved in e-learning to identify most probable facial features that represent the facial expressions or mood patterns of a student. A neural network approach is used to train the system using facial feature sets to predict specific facial expressions. Moreover, a data association based algorithm specifically for extracting information on emotional states by correlating multiple sets of facial features is also proposed. This framework showed promising results in inciting student’s interest by varying the content being delivered.Different combinations of inter-related facial expressions for specific time frames were used to estimate mood patterns and subsequently level of involvement of a student in an e-learning environment.The results achieved during the course of research showed that mood patterns of a student provide a good correlation with his interest or involvement during online lectures and can be used to vary the content to improve students’ involvement in the e-learning system.More facial expressions and mood categories can be included to diversify the application of the proposed method.
Highlights
The main problem arises in E-Learning as there is no supervisor to assess how students are physically and emotionally responding to the delivered content
When the students taking any course online, they may lose concentration and focus resulting in poor academic performance. Tackling this issue can advance the e-learning process many fold as each student‟s interest can be assessed and necessary improvements can be made to the content to engage the user during the online lecture
In order to circumvent the problem of observing student on an e-learning platform, this research is conducted with a view to analyze the relationship between facial expressions of a student enrolled in an e-learning system and the ways to improve upon learning attitude of such students using information extracted from these features
Summary
The main problem arises in E-Learning as there is no supervisor to assess how students are physically and emotionally responding to the delivered content. When the students taking any course online, they may lose concentration and focus resulting in poor academic performance. Tackling this issue can advance the e-learning process many fold as each student‟s interest can be assessed and necessary improvements can be made to the content to engage the user during the online lecture. In order to improve the e-learning experience, the process of learning becomes imperative as it majorly governs how much and how well a student can absorb knowledge during online lectures [1].Delivery of content, examinations and student feedback are important measures that have a direct effect on the learning curve of students as well as the e-learning objectives. Still the time frame required for relating and observing all these measures must be long enough to account for every possible detail [2]
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