Publications
Full Conference Papers
2020
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Peeking through the Classroom Window : A Detailed Data-Driven Analysis on the Usage of a Curriculum Integrated Math Game in Authentic Classrooms In Proceedings of The 10th ACM International Conference of Learning Analytics and Knowledge, 2020, Frankfurt, Germany. (Acceptance rate : 30.7%)[Request Full Text]
We present a data-driven analysis that provides generalized insights of how a curriculum integrated educational math game gets used as a routinized classroom activity throughout the year in authentic primary school classrooms. Our study relates observations from a field study on Spatial Temporal Math (ST Math) to our findings mined from ST Math students' sequential game play data. We identified features that vary across game play sessions and modeled their relationship with session performance. We also derived data-informed suggestions that may provide teachers with insights into how to design classroom game play sessions to facilitate more effective learning.
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Data-informed Curriculum Sequences for a Curriculum-Integrated Game In Proceedings of The 10th ACM International Conference of Learning Analytics and Knowledge, 2020, Frankfurt, Germany. (Acceptance rate : 30.7%)[Request Full Text]
In this paper, we perform a predictive analysis of a curriculum-integrated math game, ST Math, to suggest a partial ordering for the game's curriculum sequence. We analyzed the sequence of ST Math objectives played by elementary school students in 5 U.S. districts and grouped each objective into difficult and easy categories according to how many retries were needed for students to master an objective. We observed that retries on some objectives were high in one district and low in another district where the objectives are played in a different order. Motivated by this observation, we investigated what makes an effective curriculum sequence. To infer a new partially-ordered sequence, we performed an expanded replication study of a novel predictive analysis by a prior study to find predictive relationships between 15 objectives played in different sequences by 3,328 students from 5 districts. Based on the predictive abilities of objectives in these districts, we found 17 suggested objective orderings. After deriving these orderings, we confirmed the validity of the order by evaluating the impact of the suggested sequence on changes in rates of retries and corresponding performance. We observed that when the objectives were played in the suggested sequence, we record a drastic reduction in retries, implying that these objectives are easier for students. This indicates that objectives that come earlier can provide prerequisite knowledge for later objectives. We believe that data-informed sequences, such as the ones we suggest, may improve efficiency of instruction and increase content learning and performance.
Workshop Papers
2020
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The Impact of Data-driven Positive Programming Feedback: When it Helps, What Happens when it Goes Wrong, and How Students Respond In the 4th Educational Data Mining in Computer Science Education (CSEDM) Virtual Workshop in conjunction with EDM 2020[Request Full Text]
This paper uses a case-based approach to investigate the impact of data-driven positive feedback on students’ behaviour when integrated into a block-based programming environment. We embedded data-driven feature detectors to provide students with immediate positive feedback on completed objectives during programming. We deployed the system in one programming homework in a non-majors CS class. We conducted an expert analysis to determine when data-driven detectors were correct or incorrect, and investigated the impact of the system on student behavior on the homework, specifically in terms of time they spent in the system. Our results highlight when data-driven positive feedback helps students, what happens when it goes wrong, and how this impacted students’ programming behavior. Results from these case studies can shed light on the design of future data-driven systems to provide novices with the positive feedback that can help them persist while learning to program.
2019
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How Long is Enough? Predicting Student Outcomes withSame-Day Gameplay Data in an Educational Math Game In 2019 WORKSHOP ON EDM & GAMES: LEVELING UP ENGAGED LEARNING WITH DATA-RICH ANALYTICS, Montrèal, Canada.[Request Full Text]
Curriculum-integrated games can provide teachers with data to help them decide when and how to intervene with individual students. Based on our prior work observing teachers using ST Math, teachers may not be able to attend to a dashboard or student screens to determine who might need intervention. We therefore set out to determine how much data we need from the current ST Math gameplay session to predict performance. Based on the available log data that tracks student performance over SETS of puzzles, we performed two experiments to predict performance. The first uses data from one game level, which is about 3 minutes long, to predict the performance on the next level, and the second uses the first 6 minutes of gameplay to predict how many levels a student can complete in 20 minutes, a typical class length. Our results show that our data are not fine-grained enough to allow for paired level prediction, but that 6 minutes of gameplay can be used to rank students in order of performance for a class session. These results can be used as a basis for an alert system that could help teachers prioritize their time in the classroom.
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