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A, Khosravi, H., Reidsema, C., Bakharia, A., Belonogoff, M., & Fleming, M. (2018). Reciprocal peer recommendation for learning purposes. In Proceedings of the 8th international conference on learning analytics and knowledge (pp. 226\u2013235).","DOI":"10.1145\/3170358.3170400"},{"key":"10.1007\/s40593-021-00271-1_bib74","doi-asserted-by":"crossref","unstructured":"Qiu, J., Tang, J., Liu, T. X., Gong, J., Zhang, C., Zhang, Q., & Xue, Y. (2016). Modeling and predicting learning behavior in moocs. In ACM International conference on web search and data mining, WSDM (pp. 93\u2013102).","DOI":"10.1145\/2835776.2835842"},{"issue":"6","key":"10.1007\/s40593-021-00271-1_bib75","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1177\/1362168816684368","article-title":"Content familiarity, task repetition and chinese efl learners\u2019 engagement in second language use","volume":"21","author":"Qiu","year":"2017","journal-title":"Language Teaching Research"},{"issue":"2","key":"10.1007\/s40593-021-00271-1_bib76","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2019.102058","article-title":"On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms","volume":"57","author":"Ramos","year":"2020","journal-title":"Information Processing & Management"},{"key":"10.1007\/s40593-021-00271-1_bib77","doi-asserted-by":"crossref","unstructured":"Rastegarpanah, B., Gummadi, K. P., & Crovella, M. (2019). Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems. In International conference on web search and data mining, WSDM (pp. 231\u2013239). ACM.","DOI":"10.1145\/3289600.3291002"},{"key":"10.1007\/s40593-021-00271-1_bib78","unstructured":"Ren, Z., Ning, X., Lan, A. S, & Rangwala, H. (2019). Grade prediction based on cumulative knowledge and co-taken courses. International Educational Data Mining Society."},{"key":"10.1007\/s40593-021-00271-1_bib79","unstructured":"Rieckmann, M. (2018). Learning to transform the world: key competencies in education for sustainable development. Issues and trends in education for sustainable dev39."},{"key":"10.1007\/s40593-021-00271-1_bib80","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In 10Th international conference on world wide web, WWW (pp. 285\u2013295).","DOI":"10.1145\/371920.372071"},{"key":"10.1007\/s40593-021-00271-1_bib81","doi-asserted-by":"crossref","unstructured":"Sclater, N., & Bailey, P. (2015). Code of practice for learning analytics.","DOI":"10.18608\/jla.2016.31.3"},{"key":"10.1007\/s40593-021-00271-1_bib82","doi-asserted-by":"crossref","unstructured":"Selbst, A. D, Boyd, D., Friedler, S. A, Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency (pp. 59\u201368).","DOI":"10.1145\/3287560.3287598"},{"key":"10.1007\/s40593-021-00271-1_bib83","unstructured":"Shields, L., Newman, A., & Satz, D. (2017). Equality of educational opportunity."},{"key":"10.1007\/s40593-021-00271-1_bib84","unstructured":"Shum, S.B. (2018). Transitioning education\u2019s knowledge infrastructure: Shaping design or shouting from the touchline?. In Proceedings of International Conference of the Learning Sciences ICLS."},{"key":"10.1007\/s40593-021-00271-1_bib85","unstructured":"Singh, A., & Joachims, T. (2019). Policy learning for fairness in ranking. In Advances in neural information processing systems (pp. 5427\u20135437)."},{"key":"10.1007\/s40593-021-00271-1_bib86","doi-asserted-by":"crossref","unstructured":"Steck, H. (2018). Calibrated recommendations. In 12th ACM conference on recommender systems, recsys (pp. 154\u2013162).","DOI":"10.1145\/3240323.3240372"},{"key":"10.1007\/s40593-021-00271-1_bib87","series-title":"Curriculum development: Perspectives, principles and issues","author":"Talla","year":"2012"},{"key":"10.1007\/s40593-021-00271-1_bib88","unstructured":"Thaker, K., Zhang, L., He, D., & Brusilovsky, P. (2020). Recommending remedial readings using student knowledge state. In 13th international conference on educational data mining (pp. 233\u2013244)."},{"key":"10.1007\/s40593-021-00271-1_bib89","doi-asserted-by":"crossref","unstructured":"Tsai, Y.-S., & Gasevic, D. (2017). Learning analytics in higher education\u2014challenges and policies: a review of eight learning analytics policies. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 233\u2013242).","DOI":"10.1145\/3027385.3027400"},{"key":"10.1007\/s40593-021-00271-1_bib90","unstructured":"Vygotsky, L. S. (1978). Mind in society the development of higher psicologycal processes."},{"key":"10.1007\/s40593-021-00271-1_bib91","doi-asserted-by":"crossref","unstructured":"Wang, S., Wu, H., Ji, H. K., & Andersen, E. (2019). Adaptive learning material recommendation in online language education. In International conference on artificial intelligence in education (pp. 298\u2013302). Springer.","DOI":"10.1007\/978-3-030-23207-8_55"},{"issue":"4","key":"10.1007\/s40593-021-00271-1_bib92","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1080\/17439884.2017.1278020","article-title":"Decoding classdojo: psycho-policy, social-emotional learning and persuasive educational technologies","volume":"42","author":"Williamson","year":"2017","journal-title":"Learning, Media and Technology"},{"issue":"1","key":"10.1007\/s40593-021-00271-1_bib93","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/meet.2009.1450460215","article-title":"Selection of information sources Accessibility of and familiarity with sources, and types of tasks","volume":"46","author":"Xie","year":"2009","journal-title":"Proceedings of the American Society for Information Science and Technology"},{"key":"10.1007\/s40593-021-00271-1_bib94","unstructured":"Yao, S., & Huang, B. (2017). Beyond parity: Fairness objectives for collaborative filtering. In Annual conference on neural information processing systems, NIPS (pp. 2921\u20132930)."},{"key":"10.1007\/s40593-021-00271-1_bib95","unstructured":"Yu, R., Li, Q., Fischer, C., Doroudi, S., & Xu, D. (2020). Towards accurate and fair prediction of college success: evaluating different sources of student data. In Proceedings of the 13th International Conference on Educational Data Mining (EDM 2020)."},{"key":"10.1007\/s40593-021-00271-1_bib96","doi-asserted-by":"crossref","unstructured":"Zhang, J., Hao, B., Bo, C., Li, C., Chen, H., & Sun, J. (2019). Hierarchical reinforcement learning for course recommendation in moocs. In International conference on artificial intelligence, AAAI, (Vol. 33 pp. 435\u2013442).","DOI":"10.1609\/aaai.v33i01.3301435"},{"key":"10.1007\/s40593-021-00271-1_bib97","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Cao, L., Zhu, C., Li, Z., & Sun, J. (2018). Coupledcf: Learning explicit and implicit user-item couplings in recommendation for deep collaborative filtering. In International joint conference on artificial intelligence, IJCAI.","DOI":"10.24963\/ijcai.2018\/509"},{"key":"10.1007\/s40593-021-00271-1_bib98","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Hu, X., & Caverlee, J. (2018). Fairness-aware tensor-based recommendation. In International conference on information and knowledge management, CIKM (pp. 1153\u20131162). 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