Emily Jensen

Selected Publications

1. Toward Automated Feedback on Teacher Discourse to Enhance Teacher Learning

   Jensen, Emily, Dale, Meghan, Donnelly, Patrick J., Stone, Cathlyn, Kelly, Sean, Godley, Amanda,  and D’Mello, Sidney K.

   In 2020 CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2020) 2020

   Abs PDF Poster

   Like anyone, teachers need feedback to improve. Due to the high cost of human classroom observation, teachers receive infrequent feedback which is often more focused on evaluating performance than on improving practice. To address this critical barrier to teacher learning, we aim to provide teachers with detailed and actionable automated feedback. Towards this end, we developed an approach that enables teachers to easily record high-quality audio from their classes. Using this approach, teachers recorded 142 classroom sessions, of which 127 (89%) were usable. Next, we used speech recognition and machine learning to develop teacher-generalizable computer-scored estimates of key dimensions of teacher discourse. We found that automated models were moderately accurate when compared to human coders and that speech recognition errors did not influence performance. We conclude that authentic teacher discourse can be recorded and analyzed for automatic feedback. Our next step is to incorporate the automatic models into an interactive visualization tool that will provide teachers with objective feedback on the quality of their discourse.

2. What You Do Predicts How You Do: Prospectively Modeling Student Quiz Performance Using Activity Features in an Online Learning Environment

   Jensen, Emily, Umada, Tetsumichi, Hunkins, Nicholas C., D’Mello, Sidney K., Hutt, Stephen,  and Huggins-Manley, A. Corinne

   In LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21) 2021

   Abs PDF Slides

   Students using online learning environments need to effectively self-regulate their learning. However, with an absence of teacher-provided structure, students often resort to less effective, passive learning strategies versus constructive ones. We consider the potential benefits of interventions that promote retrieval practice – retrieving learned content from memory – which is an effective strategy for learning and retention. The goal is to nudge students towards completing short, formative quizzes when they are likely to succeed on those assessments. Towards this goal, we developed a machine-learning model using data from 32,685 students who used an online mathematics platform over an entire school year to prospectively predict scores on three-item assessments (N=210,020) from interaction patterns up to 9 minutes before the assessment as well as Item Response Theory (IRT) estimates of student ability and quiz difficulty. These models achieved a student-independent correlation of 0.55 between predicted and actual scores on the assessments and outperformed IRT-only predictions (r=0.34). Model performance was largely independent of the length of the analyzed window preceding a quiz. We discuss potential for future applications of the models to trigger dynamic interventions that aim to encourage students to engage with formative assessments rather than more passive learning strategies.

3. Automated Assessment and Adaptive Multimodal Formative Feedback Improves Psychomotor Skills Training Outcomes in Quadrotor Teleoperation

   Jensen, Emily, Sankaranarayanan, Sriram,  and Hayes, Bradley

   In 2024 International Conference on Human-Agent Interaction 2024