TY - GEN
T1 - Improving Students’ Self-awareness by Analyzing Course Discussion Forum Data
AU - Farahmand, Arta
AU - Dewan, M. Ali Akber
AU - Lin, Fuhua
AU - Hwang, Wu Yuin
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - The growing demand for self-paced online learning (SPOL) courses lead to post-secondary institutions exploring how information technology can be used to improve the quality of SPOL courses by evaluating teaching and learning strategies, methods, activities, and the way students engage with their studies. One of the main barriers in SPOL is that students may feel isolated as they learn in an individualized mode and collaborative learning could be difficult to realize. The isolation may be lessened when students interact in a course discussion forum. To improve students’ self-awareness, it is needed to collect and analyze the forum data and visualize students’ sentiments to provide feedback to the students. This paper presents a model for sentiment analysis using natural language processing techniques to visualize students’ affective states towards the course as a strategy to enhance students’ self-awareness. We used the Stanford MOOC Posts dataset, from Stanford University’s eleven public online courses to test the proposed model. Finally, the paper presents a method to visualize the insights gained from the analysis in a student-facing intelligent learning dashboard (SF-iLDs) to support students’ self-awareness of their sentiment towards the course to encourage adjustments to the level of engagement.
AB - The growing demand for self-paced online learning (SPOL) courses lead to post-secondary institutions exploring how information technology can be used to improve the quality of SPOL courses by evaluating teaching and learning strategies, methods, activities, and the way students engage with their studies. One of the main barriers in SPOL is that students may feel isolated as they learn in an individualized mode and collaborative learning could be difficult to realize. The isolation may be lessened when students interact in a course discussion forum. To improve students’ self-awareness, it is needed to collect and analyze the forum data and visualize students’ sentiments to provide feedback to the students. This paper presents a model for sentiment analysis using natural language processing techniques to visualize students’ affective states towards the course as a strategy to enhance students’ self-awareness. We used the Stanford MOOC Posts dataset, from Stanford University’s eleven public online courses to test the proposed model. Finally, the paper presents a method to visualize the insights gained from the analysis in a student-facing intelligent learning dashboard (SF-iLDs) to support students’ self-awareness of their sentiment towards the course to encourage adjustments to the level of engagement.
KW - Natural language processing
KW - self-paced online learning
KW - sentiment analysis
KW - student engagement
UR - http://www.scopus.com/inward/record.url?scp=85171452616&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34735-1_1
DO - 10.1007/978-3-031-34735-1_1
M3 - Published Conference contribution
AN - SCOPUS:85171452616
SN - 9783031347344
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 14
BT - Adaptive Instructional Systems - 5th International Conference, AIS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Proceedings
A2 - Sottilare, Robert A.
A2 - Schwarz, Jessica
T2 - 5th International Conference on Adaptive Instructional Systems, AIS 2023, held as part of the 25th International Conference on Human-Computer Interaction, HCII 2023
Y2 - 23 July 2023 through 28 July 2023
ER -