Marion Neumann
ABSTRACT
Enrollment numbers in computer science courses are higher than ever and keep growing. This renders communication and interaction of instructors with individual students extremely challenging, leading to an increase in anonymity (anonymity gap). Especially when students struggle in computing courses, personalized help is crucial for them to overcome their problems and frustration and eventually succeed in their studies. At the same time detecting students' misconceptions and gathering feedback at scale is time consuming, resulting in a lack of unbiased feedback available to course instructors (feedback gap). Real-time student feedback is a crucial source for instructors to adapt their teaching pace, teaching materials, or course content during the course of the semester to cater to an increasingly diverse student population. In this paper, we investigate a scalable approach to collect and analyze student feedback and emotions. We find that sentiment analysis can efficiently capture student emotions, bearing the potential to lessen both the anonymity and feedback gaps.
- Paola Adinolfi, Ernesto D'Avanzo, Miltiadis D Lytras, Isabel Novo-Corti, and Jose Picatoste. 2016. Sentiment analysis to evaluate teaching performance. International Journal of Knowledge Society Research (IJKSR), Vol. 7, 4 (2016), 86--107.Google Scholar
Digital Library
- Nabeela Altrabsheh, Mihaela Cocea, and Sanaz Fallahkhair. 2014a. Learning sentiment from students' feedback for real-time interventions in classrooms. In International Conference on Adaptive and Intelligent Systems. Springer, 40--49.Google ScholarCross Ref
- Nabeela Altrabsheh, Mihaela Cocea, and Sanaz Fallahkhair. 2014b. Sentiment analysis: towards a tool for analysing real-time students feedback. In 2014 IEEE 26th international conference on tools with artificial intelligence. IEEE, 419--423.Google ScholarDigital Library
- Computing Research Association et al. 2017. Generation CS: Computer Science Undergraduate Enrollments Surge Since 2006.Google Scholar
- Ryan SJd Baker, Sidney K D'Mello, Ma Mercedes T Rodrigo, and Arthur C Graesser. 2010. Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive--affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, Vol. 68, 4 (2010), 223--241.Google ScholarDigital Library
- Francis F Balahadia, Ma Corazon G Fernando, and Irish C Juanatas. 2016. Teacher's performance evaluation tool using opinion mining with sentiment analysis. In 2016 IEEE Region 10 Symposium (TENSYMP). IEEE, 95--98.Google ScholarCross Ref
- Jens Bennedsen and Michael E Caspersen. 2008. Optimists have more fun, but do they learn better? On the influence of emotional and social factors on learning introductory computer science. Computer Science Education, Vol. 18, 1 (2008), 1--16.Google ScholarCross Ref
- Ida Camacho and Ashok Goel. 2018. Longitudinal trends in sentiment polarity and readability of an Online Masters of Computer Science course. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale. ACM, 21.Google ScholarDigital Library
- Erik Cambria, Björn Schuller, Yunqing Xia, and Catherine Havasi. 2013. New avenues in opinion mining and sentiment analysis. IEEE Intelligent systems, Vol. 28, 2 (2013), 15--21.Google ScholarDigital Library
- V Dhanalakshmi, Dhivya Bino, and AM Saravanan. 2016. Opinion mining from student feedback data using supervised learning algorithms. In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, 1--5.Google ScholarCross Ref
- Brian Dorn and Allison Elliott Tew. 2015. Empirical validation and application of the computing attitudes survey. Computer Science Education, Vol. 25, 1 (2015), 1--36.Google ScholarCross Ref
- Bruce Gerson. 2019. Life@Carnegie Mellon University Measures Student Stress, Depression. https://www.cmu.edu/news/stories/archives/2019/april/life-at-cmu-project.html [Online; viewed 24-June-2019].Google Scholar
- Julie Gould. 2014. Mental health: Stressed students reach out for help. Nature, Vol. 512, 7513 (2014), 223--224.Google Scholar
- Christina Hinton, Koji Miyamoto, and BRUNO Della-Chiesa. 2008. Brain research, learning and emotions: implications for education research, policy and practice 1. European Journal of education, Vol. 43, 1 (2008), 87--103.Google Scholar
- Clayton J Hutto and Eric Gilbert. 2014. Vader: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. In Eighth international AAAI conference on weblogs and social media.Google Scholar
- Paivi Kinnunen and Beth Simon. 2010. Experiencing programming assignments in CS1: the emotional toll. In Proceedings of the Sixth international workshop on Computing education research. ACM, 77--86.Google ScholarDigital Library
- P"aivi Kinnunen and Beth Simon. 2011. CS majors' self-efficacy perceptions in CS1: results in light of social cognitive theory. In Proceedings of the seventh international workshop on Computing education research. ACM, 19--26.Google Scholar
- P"aivi Kinnunen and Beth Simon. 2012. My program is ok--am I? Computing freshmen's experiences of doing programming assignments. Computer Science Education, Vol. 22, 1 (2012), 1--28.Google ScholarCross Ref
- Alex Lishinski, Aman Yadav, and Richard Enbody. 2017. Students' emotional reactions to programming projects in introduction to programming: Measurement approach and influence on learning outcomes. In Proceedings of the 2017 ACM Conference on International Computing Education Research. ACM, 30--38.Google ScholarDigital Library
- Bing Liu. 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, Vol. 5, 1 (2012), 1--167.Google ScholarDigital Library
- Luis M Lozano, Eduardo Garc'ia-Cueto, and José Mu niz. 2008. Effect of the number of response categories on the reliability and validity of rating scales. Methodology, Vol. 4, 2 (2008), 73--79.Google ScholarCross Ref
- Sunghwan Mac Kim and Rafael A Calvo. 2010. Sentiment Analysis in Student Experiences of Learning.. In EDM. 111--120.Google Scholar
- Carolina Mega, Lucia Ronconi, and Rossana De Beni. 2014. What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of educational psychology, Vol. 106, 1 (2014), 121.Google ScholarCross Ref
- Myriam Munezero, Calkin Suero Montero, Maxim Mozgovoy, and Erkki Sutinen. 2013. Exploiting sentiment analysis to track emotions in students' learning diaries. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research. ACM, 145--152.Google ScholarDigital Library
- Julia Nguyen. 2014. The Pressures of Success in Undergraduate Computer Science Programs. https://modelviewculture.com/pieces/the-pressures-of-success-in-undergraduate-computer-science-programs [Online; viewed 24-June-2019].Google Scholar
- Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 79--86.Google ScholarDigital Library
- Dale Parsons, Krissi Wood, Joy Gasson, and Adon Moskal. 2019. Development of a Self-Reporting Tool for Capturing Student Emotions During Programming Activities. In Proceedings of the Twenty-First Australasian Computing Education Conference. ACM, 64--68.Google ScholarDigital Library
- Reinhard Pekrun, Andrew J Elliot, and Markus A Maier. 2009. Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of educational Psychology, Vol. 101, 1 (2009), 115.Google ScholarCross Ref
- Fahmi Candra Permana, Yusep Rosmansyah, and Atje Setiawan Abdullah. 2017. Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media. In Journal of Physics: Conference Series, Vol. 893. IOP Publishing, 012051.Google Scholar
- Elizabeth R Peterson, Gavin TL Brown, and Miriam C Jun. 2015. Achievement emotions in higher education: A diary study exploring emotions across an assessment event. Contemporary Educational Psychology, Vol. 42 (2015), 82--96.Google ScholarCross Ref
- Sujata Rani and Parteek Kumar. 2017. A sentiment analysis system to improve teaching and learning. Computer, Vol. 50, 5 (2017), 36--43.Google ScholarDigital Library
- Diana Schindler, Lars Lüpke, and Reinhold Decker. 2014. Estimating true ratings from online consumer reviews. In German-Japanese Interchange of Data Analysis Results. Springer, 235--252.Google Scholar
- Michael A Stephens. 1974. EDF statistics for goodness of fit and some comparisons. Journal of the American statistical Association, Vol. 69, 347 (1974), 730--737.Google ScholarCross Ref
- Andranik Tumasjan, Timm O Sprenger, Philipp G Sandner, and Isabell M Welpe. 2010. Predicting elections with twitter: What 140 characters reveal about political sentiment. In Fourth international AAAI conference on weblogs and social media.Google ScholarCross Ref
- Rachel Zimmerman. 2012. MIT 'Meltdown' Blog Resonates With Stressed-Out Students. https://www.wbur.org/commonhealth/2012/11/09/mit-meltdown-blog-stressed-students [Online; viewed 24-June-2019].Google Scholar
- Capturing Student Feedback and Emotions in Large Computing Courses: A Sentiment Analysis Approach
Recommendations
Sentiment Analysis to Track Emotion and Polarity in Student Fora
PCI '17: Proceedings of the 21st Pan-Hellenic Conference on InformaticsThe purpose of this paper is to propose a data mining methodology for analysing data relating to the participation of students in the online forum of a postgraduate course at the Hellenic Open University. Data is migrated to MongoDB, a NoSQL database ...
Emotions in Online Gambling Communities: A Multilevel Sentiment Analysis
Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network AnalysisAbstractIn this study, we analyzed whether interaction dynamics are related to emotional expressions within online gambling communities. As data, we used 8452 comments posted on Reddit gambling communities. The data were analyzed with sentiment analysis ...
A Sentiment Analysis Based Approach for Exploring Student Feedback
Innovative Technologies and LearningAbstractStudent feedback is commonly used as a reliable source of information to evaluate learning outcomes and teaching quality. This feedback has proven to provide faculty not only with valuable insights into how students are learning, but also with an ...
Comments