Abstract
Few could have anticipated the sudden and dramatic impact of COVID-19 on all aspects of life, including online academic help-seeking of institutional education. Academic help-seeking is a quite prevalent phenomenon that supports students to learn knowledge and improve academic performance. This study is aiming to understand learners and associate their performances via exploiting academic help-seeking moods with online learning of institutional education setting. Adopting the relevant theories, we propose a novel research model and identify three different online help-seeking moods, which are namely goal-directed seeker, exploratory seeker and avoidant seeker. Goal-directed seekers are described with preference for more challenging assignments and more posting on the platform discussion board frequently. Exploratory seekers hold the highest achievements during all help-seeking moods. Avoidant seekers are well-distinguished by holding the lowest frequency of posting among all moods and the most average time spent on the platform. Students have collective preferences for assignment submission in each help-seeking mood, and deviation from those preferences increases their probability of dropping academic grade significantly. To the best of our knowledge, this research is the first work that characterizes the help-seeking moods and associates moods with the enrollment performance for online education of institutional student.
Keywords: Help-seeker, Information Seeking, Online learning, Academic Resources, Education Informatization
References
- 1.Tang Lumin, Yu Ruonan, Dong Qiwen, Hong Daocheng, Fu Yunbin. “A review of non-intrusive sensing based personalized resource recommendations for help-seekers in education.”. Journal of East China Normal University(Natural Science) 2018;2018(5):17–29. [Google Scholar]
- 2.Srba Ivan, Bielikova Maria. “A Comprehensive Survey and Classification of Approaches for Community Question Answering.”. ACM Transactions on the Web. 2016;10(3):1–28. doi: 10.1145/2934687. [DOI] [Google Scholar]
- 3.Hadi Mogavi Reza, Ma Xiaojuan, Hui Pan. “Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites.”. Journal of ACM. 2021;37(4):22. doi: 10.1145/1122445.1122456. Article 111 (August 2021), [DOI] [Google Scholar]
- 4.Yuanzhe Chen, Qing Chen, Mingqian Zhao, Sebastien Boyer, Kalyan Veeramachaneni, and Huamin Qu. (2016) “Dropout-Seer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction.” In proceedings of IEEE Conference on Visual Analytics Science and Technology IEEE, 111–120.
- 5.Reza Hadi Mogavi, Sujit Gujar, Xiaojuan Ma, and Pan Hui. (2019) “HRCR: Hidden Markov-Based Reinforcement to Reduce Churn in Question Answering Forums.” In Proceedings of Pacific Rim International Conference on Artificial Intelligence 364–376. 10.1007/978-3-030-29908-8_29 [DOI]
- 6.K Makara, Karabenick S.A. In: Advances in help-seeking research and applications: The role of emerging technologies. Karabenick S.A., Puustinen M., editors. Information Age Publishing; Charlotte, NC: 2013. “Characterizing sources of academic help in the age of expanding educational technology: A new conceptual framework.”. [Google Scholar]
- 7.Karabenick A. “Relationship of academic help seeking to the use of learning strategies and other instrumental achievement behavior in college students.”. Journal of Educational Psychology. 1991;83(2):221–230. [Google Scholar]
- 8.A Ryan, Shin H. “Help-seeking tendencies during early adolescence: An examination of motivational correlates and consequences for achievement.”. Learning & Instruction. 2011;21(2):247–256. [Google Scholar]
- 9.Nese Alyuz, Eda Okur, Utku Genc, Sinem Aslan, Cagri Tanriover, and Asli Arslan Esme. (2017) “An Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students.” In Proceedings of the ACM SIGCHI International Workshop on Multimodal Interaction for Education (Glasgow, UK) (MIE 2017). Association for Computing Machinery, New York, NY, USA, 26–32. 10.1145/3139513.3139521 [DOI]
- 10.Sinem Aslan, Nese Alyuz, Cagri Tanriover, Sinem E. Mete, Eda Okur, Sidney K. D’Mello, and Asli Arslan Esme. (2019) “Investigating the Impact of a Real-Time, Multimodal Student Engagement Analytics Technology in Authentic Classrooms.” In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ‘19). Association for Computing Machinery, New York, NY, USA, 1–12. 10.1145/3290605.3300534 [DOI]
- 11.Waldrop Devin, Reschly Amy L., Fraysier Kathleen, Appleton James J. “Measuring the Engagement of College Students: Administration Format, Structure, and Validity of the Student Engagement Instrument–College.”. Measurement and Evaluation in Counseling and Development. 2018;52(2):90–107. doi: 10.1080/07481756.2018.1497429. [DOI] [Google Scholar]
- 12.Moubayed Abdallah, Injadat Mohammadnoor, Shami Abdallah, Lutfiyya Hanan. “Student Engagement Level in an e-Learning Environment: Clustering Using K-means.”. American Journal of Distance Education. 2020;34(2):137–156. doi: 10.1080/08923647.2020.1696140. [DOI] [Google Scholar]
- 13.Adabriand Furtado, Nazareno Andrade, Nigini Oliveira, and Francisco Brasileiro. (2013) “Contributor Profiles, Their Dynamics, and Their Importance in Five Q&a Sites.” In Proceedings of the Conference on Computer Supported Cooperative Work (San Antonio, Texas, USA) Association for Computing Machinery, New York, NY, USA, 1237–1252. 10.1145/2441776.2441916 [DOI]
- 14.Louis Faucon, Lukasz Kidzinski, and Pierre Dillenbourg. (2016) “Semi-Markov Model for Simulating MOOC Students.” In proceedings of the 9th International Conference on Educational Data Mining, Raleigh, USA, June 30 - July 2, International Educational Data Mining Society.
- 15.Daocheng Hong, Yang Li, Qiwen Dong. (2020) “Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation.” In Proceedings of Conference on Research and Development in Information Retrieval (SIGIR’20), July 25-30, 2020. Virtual Event, China. ACM, New York, NY, USA. 1721–1725 10.1145/3397271.3401225 [DOI]
- 16.Baum Leonard E., Petrie Ted, Soules George, Weiss Norman. “A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains.”. The Annals of Mathematical Statistics. 1970;41(1):164–171. http://www.jstor.org/stable/2239727. [Google Scholar]
- 17.Marios Kokkodis. (2019) “Reputation Deflation Through Dynamic Expertise Assessment in Online Labor Markets.” In The World Wide Web Conference (San Francisco, CA, USA) (WWW ‘19). ACM, New York, NY, USA, 896–905. 10.1145/3308558.3313479 [DOI]
- 18.Xi Zhang, Shan Jiang, and Yihang Cheng. (2017) “Inferring the Student Social Loafing State in Collaborative Learning with a Hidden Markov Model: A Case on Slack.” In Proceedings of the International Conference on World Wide Web Companion (Perth, Australia). 149–152. 10.1145/3041021.3054145 [DOI]
- 19.Nate Gruver, Ali Malik, Brahm Capoor, Chris Piech, Mitchell L Stevens, and Andreas Paepcke. (2019) “Using Latent Variable Models to Observe Academic Pathways.” In Proceedings of The International Conference on Educational Data Mining. EDM, 294–299. http://educationaldatamining.org/edm2019/proceedings/
- 20.Shang Junfeng, Cavanaugh Joseph E. “Bootstrap variants of the Akaike information criterion for mixed model selection.”. Computational Statistics & Data Analysis. 2008;52(4):2004–2021. doi: 10.1016/j.csda.2007.06.019. [DOI] [Google Scholar]
- 21.Scott Chen, Ponani Gopalakrishnan, et al. (1998) “Speaker, environment and channel change detection and clustering via the bayesian information criterion.” In Proc. DARPA Broadcast News Transcription and Understanding Workshop Virginia, USA, 127–132.
- 22.Liu C.H.B., Chamberlain B.P., McCoy E.J. “What is the Value of Experimentation and Measurement?”. Data Science and Engineering. 2020;5(2):152–167. [Google Scholar]
