ABSTRACT
With cross-disciplinary academic interests increasing and academic advising resources over capacity, the importance of exploring data-assisted methods to support student decision making has never been higher. We build on the findings and methodologies of a quickly developing literature around prediction and recommendation in higher education and develop a novel recurrent neural network-based recommendation system for suggesting courses to help students prepare for target courses of interest, personalized to their estimated prior knowledge background and zone of proximal development. We validate the model using tests of grade prediction and the ability to recover prerequisite relationships articulated by the university. In the third validation, we run the fully personalized recommendation for students the semester before taking a historically difficult course and observe differential overlap with our would-be suggestions. While not proof of causal effectiveness, these three evaluation perspectives on the performance of the goal-based model build confidence and bring us one step closer to deployment of this personalized course preparation affordance in the wild.
- Lalitha Agnihotri, Alfred Essa, and Ryan Baker. 2017. Impact of Student Choice of Content Adoption Delay on Course Outcomes. In Proceedings of the Seventh International Learning Analytics and Knowledge Conference (LAK '17). ACM. Google ScholarDigital Library
- Bita Akram, Bradford Mott, Wookhee Min, Kristy Elizabeth Boyer, Eric Wiebe, and James Lester. 2018. Improving Stealth Assessment in Game-based Learning with LSTM-based Analytics. In Proceedings of the 11th International Educational Data Mining Conference. Pp. 208--218.Google Scholar
- Juan Miguel L. Andres, Ryan S. Baker, Dragan Gašević, George Siemens, Scott A. Crossley, and Srećko Joksimović. 2018. Studying MOOC Completion at Scale Using the MOOC Replication Framework. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK '18). ACM. Google ScholarDigital Library
- Michael Backenköhler, Felix Scherzinger, Adish Singla, and Verena Wolf. 2018. Data-Driven Approach Towards a Personalized Curriculum. In Proceedings of the 11th EDM Conference.Google ScholarCross Ref
- Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of machine learning research 3, Feb (2003), 1137--1155. Google Scholar
- Michael Geoffrey Brown, R. Matthew DeMonbrun, and Stephanie D. Teasley. 2018. Conceptualizing Co-enrollment: Accounting for Student Experiences Across the Curriculum. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK '18). ACM. Google ScholarDigital Library
- Seth Chaiklin. 2003. The zone of proximal development in Vygotsky's analysis of learning and instruction. Vygotsky's educational theory in cultural context 1 (2003), 39--64.Google Scholar
- Sorathan Chaturapruek, Thomas Dee, Ramesh Johari, René Kizilcec, and Mitchell Stevens. 2018. How a data-driven course planning tool affects college students' GPA: evidence from two field experiments. In Proceedings of the 5th International Conference on Learning @ Scale. Google ScholarDigital Library
- Weiyu Chen, Andrew S Lan, Da Cao, Christopher Brinton, and Mung Chiang. 2018. Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams. In Proceedings of the 11th EDM Conference.Google Scholar
- Aurora Esteban, Amelia Zafra, and Cristóbal Romero. 2018. A Hybrid Multi-Criteria approach using a Genetic Algorithm for Recommending Courses to University Students. In Proceedings of the 11th EDM Conference.Google Scholar
- Josh Gardner and Christopher Brooks. 2018. Coenrollment Networks and Their Relationship to Grades in Undergraduate Education. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK '18). ACM. Google ScholarDigital Library
- Josh Gardner and Christopher Brooks. 2018. Evaluating Predictive Models of Student Success: Closing the Methodological Gap. Journal of learning Analytics 5, 2 (2018), 105--125.Google ScholarCross Ref
- Josh Gardner and Christopher Brooks. 2018. Student success prediction in MOOCs. User Modeling and User-Adapted Interaction 28, 2 (2018), 127--203. Google ScholarDigital Library
- Felix A Gers, Jürgen Schmidhuber, and Fred Cummins. 1999. Learning to forget: Continual prediction with LSTM. (1999).Google Scholar
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In ICLR.Google Scholar
- Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 241--248. Google ScholarDigital Library
- Martin Hlosta, Zdenek Zdrahal, and Jaroslav Zendulka. {n. d.}. Ouroboros: Early Identification of At-risk Students Without Models Based on Legacy Data. In Proceedings of the Seventh International Learning Analytics and Knowledge Conference. Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Sandeep M Jayaprakash, Erik W Moody, Eitel JM Lauría, James R Regan, and Joshua D Baron. 2014. Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics 1, 1 (2014), 6--47.Google ScholarCross Ref
- Christopher V Le, Zachary A Pardos, Samuel D Meyer, and Rachel Thorp. 2018. Communication at Scale in a MOOC Using Predictive Engagement Analytics. In International Conference on Artificial Intelligence in Education. Springer, 239--252.Google ScholarCross Ref
- Yuetian Luo and Zachary A. Pardos. 2018. Diagnosing University Student Subject Proficiency and Predicting Degree Completion in Vector Space.. In Proceedings of the Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI). AAAI Press, Pp. 7920--7927.Google Scholar
- Ivana Ognjanovic, Dragan Gasevic, and Shane Dawson. 2016. Using institutional data to predict student course selections in higher education. The Internet and Higher Education 29 (2016), 49--62.Google ScholarCross Ref
- Zachary A Pardos, Zihao Fan, and Weijie Jiang. In press. Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance. User Modeling and User-Adapted Interaction (In press). https://arxiv.org/abs/1803.09535Google Scholar
- Zachary A Pardos, Steven Tang, Daniel Davis, and Christopher Vu Le. 2017. Enabling real-time adaptivity in MOOCs with a personalized next-step recommendation framework. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale. ACM, 23--32. Google ScholarDigital Library
- Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. neural information processing systems (2015), 505--513. Google ScholarDigital Library
- Agoritsa Polyzou and George Karypis. 2018. Feature extraction for classifying students based on their academic performance. In Proceedings of the 11th EDM Conference.Google Scholar
- Li Zhang and Huzefa Rangwala. 2018. Early Identification of At-Risk Students Using Iterative Logistic Regression. In International Conference on Artificial Intelligence in Education. 613--626.Google Scholar
Recommendations
Course Recommendation Based on Graph Convolutional Neural Network
Advances and Trends in Artificial Intelligence. Theory and ApplicationsAbstractSelecting the right learning content according to learners’ learning abilities and interests is the first and most important factor in achieving good learning performance. Based on the similarity between the course rating data in the Collaborative ...
Typicality-Based Collaborative Filtering Recommendation
Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas ...
Personalized recommendation based on review topics
The traditional collaborative filtering algorithm is a successful recommendation technology. The core idea of this algorithm is to calculate user or item similarity based on user ratings and then to predict ratings and recommend items based on similar ...
Comments