Commenced in January 2007
Paper Count: 32579
Pattern Discovery from Student Feedback: Identifying Factors to Improve Student Emotions in Learning
Abstract:Interest in (STEM) Science Technology Engineering Mathematics education especially Computer Science education has seen a drastic increase across the country. This fuels effort towards recruiting and admitting a diverse population of students. Thus the changing conditions in terms of the student population, diversity and the expected teaching and learning outcomes give the platform for use of Innovative Teaching models and technologies. It is necessary that these methods adapted should also concentrate on raising quality of such innovations and have positive impact on student learning. Light-Weight Team is an Active Learning Pedagogy, which is considered to be low-stake activity and has very little or no direct impact on student grades. Emotion plays a major role in student’s motivation to learning. In this work we use the student feedback data with emotion classification using surveys at a public research institution in the United States. We use Actionable Pattern Discovery method for this purpose. Actionable patterns are patterns that provide suggestions in the form of rules to help the user achieve better outcomes. The proposed method provides meaningful insight in terms of changes that can be incorporated in the Light-Weight team activities, resources utilized in the course. The results suggest how to enhance student emotions to a more positive state, in particular focuses on the emotions ‘Trust’ and ‘Joy’. Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 420
 V. Svenja, K. David, K. Eckhard, and B. Sonja, TALIS teaching practices and pedagogical Innovations evidence from TALIS: Evidence from TALIS. OECD Publishing, 2012.
 P. Serdyukov, “Innovation in education: what works, what doesnt, and what to do about it?” Journal of Research in Innovative Teaching & Learning, vol. 10, no. 1, pp. 4–33, 2017.
 M. B. Miles, Innovation in education. Bureau of Publication, Teachers College, Columbia University, 1964.
 C. Latulipe, N. B. Long, and C. E. Seminario, “Structuring flipped classes with lightweight teams and gamification,” in Proceedings of the 46th ACM Technical Symposium on Computer Science Education. ACM, 2015, pp. 392–397.
 E. Santhanam, B. Lynch, and J. Jones, “Making sense of student feedback using text analysis–adapting and expanding a common lexicon,” Quality Assurance in Education, vol. 26, no. 1, pp. 60–69, 2018.
 L. Alderman, S. Towers, and S. Bannah, “Student feedback systems in higher education: A focused literature review and environmental scan,” Quality in Higher Education, vol. 18, no. 3, pp. 261–280, 2012.
 L. Harvey, “The nexus of feedback and improvement,” in Student Feedback. Elsevier, 2011, pp. 3–26.
 S. Palmer, “Student evaluation of teaching: keeping in touch with reality,” Quality in higher education, vol. 18, no. 3, pp. 297–311, 2012.
 Z. W. Ras, A. Dardzinska, L.-S. Tsay, and H. Wasyluk, “Association action rules,” in IEEE/ICDM Workshop on Mining Complex Data (MCD 2008), Pisa, Italy, ICDM Workshops Proceedings,. IEEE Computer Society, 2008, pp. 283–290.
 J. Ranganathan, A. S. Irudayaraj, A. Bagavathi, and A. A. Tzacheva, “Actionable pattern discovery for sentiment analysis on twitter data in clustered environment,” Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 2849–2863, 2018.
 J. Ranganathan and A. Tzacheva, “Emotion mining in social media data,” in Proceedings of the 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems: KES2019, Procedia Computer Science journal, vol. 159. Elsevier, 2019, pp. 58–66.
 B. Bakhshinategh, O. R. Zaiane, S. ElAtia, and D. Ipperciel, “Educational data mining applications and tasks: A survey of the last 10 years,” Education and Information Technologies, vol. 23, no. 1, pp. 537–553, 2018.
 L. D. Miller, L.-K. Soh, A. Samal, K. Kupzyk, and G. Nugent, “A comparison of educational statistics and data mining approaches to identify characteristics that impact online learning.” Journal of Educational Data Mining, vol. 7, no. 3, pp. 117–150, 2015.
 M. Cocea and S. Weibelzahl, “Can log files analysis estimate learners’ level of motivation?” in LWA. University of Hildesheim, Institute of Computer Science, 2006, pp. 32–35.
 J. Bravo and A. Ortigosa, “Detecting symptoms of low performance using production rules.” International working group on educational data mining, 2009.
 B. Jagtap and V. Dhotre, “Svm and hmm based hybrid approach of sentiment analysis for teacher feedback assessment,” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol. 3, no. 3, pp. 229–232, 2014.
 Q. Rajput, S. Haider, and S. Ghani, “Lexicon-based sentiment analysis of teachers evaluation,” Applied Computational Intelligence and Soft Computing, vol. 2016, p. 1, 2016.
 A. Tzacheva, J. Ranganathan, and R. Jadi, “Multi-label emotion mining from student comments,” in Proceedings of the 2019 4th International Conference on Information and Education Innovations, ser. ICIEI 2019. New York, NY, USA: ACM, 2019, pp. 120–124.
[Online]. Available: http://doi.acm.org/10.1145/3345094.3345112
 A. Tzacheva and J. Ranganathan, “Emotion mining from student comments a lexicon based approach for pedagogical innovation assessment,” The European Journal of Education and Applied Psychology, pp. 3–13, 2018.
 J. Ranganathan, S. Sharma, and A. Tzacheva, “Hybrid action rules: Rule based and object based,” in Proceedings of the 2020 International Conference on Compute and Data Analysis. ICCDA, 2020, pp. 1–5.
 M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, “Spark: Cluster computing with working sets.” HotCloud, vol. 10, no. 10-10, p. 95, 2010.