| Affective states and state tests: Investigating how affect and engagement during the school year predict end-of-year learning outcomes. ZA Pardos, RSJD Baker, MOCZ San Pedro, SM Gowda, SM Gowda Journal of Learning Analytics 1 (1), 107-128, 2014 | 224 | 2014 |
| Population validity for educational data mining models: A case study in affect detection J Ocumpaugh, R Baker, S Gowda, N Heffernan, C Heffernan British Journal of Educational Technology 45 (3), 487-501, 2014 | 216 | 2014 |
| Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra. RSJ d Baker, SM Gowda, M Wixon, J Kalka, AZ Wagner, A Salvi, V Aleven, ... International Educational Data Mining Society, 2012 | 207 | 2012 |
| Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes ZA Pardos, RSJD Baker, MOCZ San Pedro, SM Gowda, SM Gowda Proceedings of the third international conference on learning analytics and
, 2013 | 204 | 2013 |
| Contextual slip and prediction of student performance after use of an intelligent tutor RSJ Baker, AT Corbett, SM Gowda, AZ Wagner, BA MacLaren, ... User Modeling, Adaptation, and Personalization: 18th International
, 2010 | 136 | 2010 |
| The sum is greater than the parts: Ensembling models of student knowledge in educational software ZA Pardos, SM Gowda, RSJ Baker, NT Heffernan ACM SIGKDD explorations newsletter 13 (2), 37-44, 2012 | 111 | 2012 |
| Automatically detecting a student's preparation for future learning: Help use is key RSJD Baker, S Gowda, A Corbett Educational data mining 2011, 2010 | 102 | 2010 |
| Towards predicting future transfer of learning RSJ Baker, SM Gowda, AT Corbett Artificial Intelligence in Education: 15th International Conference, AIED
, 2011 | 68 | 2011 |
| Ensembling predictions of student knowledge within intelligent tutoring systems RSJ Baker, ZA Pardos, SM Gowda, BB Nooraei, NT Heffernan User Modeling, Adaption and Personalization: 19th International Conference
, 2011 | 68 | 2011 |
| Towards an understanding of affect and knowledge from student interaction with an intelligent tutoring system MOZ San Pedro, RSJ Baker, SM Gowda, NT Heffernan Artificial Intelligence in Education: 16th International Conference, AIED
, 2013 | 56 | 2013 |
| Sensor-free automated detection of affect in a Cognitive Tutor for Algebra RSJD Baker, S Gowda, M Wixon, J Kalka, A Wagner, A Salvi, V Aleven, ... Educational Data Mining 2012, 2012 | 56 | 2012 |
| Towards automatically detecting whether student learning is shallow RSJD Baker, SM Gowda, AT Corbett, J Ocumpaugh Intelligent Tutoring Systems: 11th International Conference, ITS 2012
, 2012 | 49 | 2012 |
| Predicting K-12 dropout RS Baker, AW Berning, SM Gowda, S Zhang, A Hawn Journal of Education for Students Placed at Risk (JESPAR) 25 (1), 28-54, 2020 | 46 | 2020 |
| Predicting robust learning with the visual form of the moment-by-moment learning curve RS Baker, A Hershkovitz, LM Rossi, AB Goldstein, SM Gowda Journal of the Learning Sciences 22 (4), 639-666, 2013 | 43 | 2013 |
| Extending log-based affect detection to a multi-user virtual environment for science RS Baker, J Ocumpaugh, SM Gowda, AM Kamarainen, SJ Metcalf User Modeling, Adaptation, and Personalization: 22nd International
, 2014 | 38 | 2014 |
| An analysis of the differences in the frequency of students' disengagement in urban, rural, and suburban high schools RSJ Baker, SM Gowda Educational data mining 2010, 2010 | 37 | 2010 |
| Predicting future learning better using quantitative analysis of moment-by-moment learning A Hershkovitz, R Baker, SM Gowda, AT Corbett Educational data mining 2013, 2013 | 24 | 2013 |
| Towards automatically detecting whether student learning is shallow SM Gowda, RS Baker, AT Corbett, LM Rossi International journal of artificial intelligence in education 23, 50-70, 2013 | 23 | 2013 |
| Ensembling Predictions of Student Post-Test Scores for an Intelligent Tutoring System. ZA Pardos, SM Gowda, RSJ de Baker, NT Heffernan EDM, 189-198, 2011 | 23 | 2011 |
| Exploring the relationships between design, students affective states, and disengaged behaviors within an ITS LS Doddannara, SM Gowda, RSJ Baker, SM Gowda, AMJB De Carvalho Artificial Intelligence in Education: 16th International Conference, AIED
, 2013 | 21 | 2013 |
Ryan BakerUniversity of PennsylvaniaPatvirtintas el. paštas upenn.edu
