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

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   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

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   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. Large Language Models Enable Automated Formative Feedback in Human-Robot Interaction Tasks

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

   In Human-Large Language Model Interaction workshop at the 2024 ACM/IEEE International Conference on Human-Robot Interaction Mar 2024

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   Representing knowledge and assessing someone’s ability in an HRI task is difficult, due to complex objectives and high variability in human performance. In previous work, we begin to address this question by breaking down HRI tasks into objective primitives that can be combined sequentially and concurrently (e.g., maintain slow speed and reach waypoints). They then show that signal temporal logic specifications, paired with a robustness metric, are a useful tool for assessing performance along each primitive. These formal methods allow designers to precisely represent ideal trajectories. This formulation admits explainability, as one can identify and elaborate upon specific objectives that learners did not accomplish. We claim that LLMs can be paired with formal analysis methods to provide accessible, relevant feedback for HRI tasks. While logic specifications are useful for defining and assessing a task, these representations are not easily interpreted by non-experts. Luckily, LLMs are adept at generating easy-to-understand text that explains difficult concepts. By integrating task assessment outcomes and other contextual information into an LLM prompt, we can effectively synthesize a useful set of recommendations for the learner to improve their performance