Emily Jensen

Selected Publications

1. CHI

   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. LAK

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

   Jensen, Emily, 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 po- tential 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 as- sessment as well as Item Response Theory (IRT) estimates of stu- dent ability and quiz difficulty. These models achieved a student- independent correlation of0.55 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 interven- tions that aim to encourage students to engage with formative assessments rather than more passive learning strategies.

3. CPHS

   Using Artificial Potential Fields To Model Driver Situational Awareness

   Jensen, Emily, Luster, Maya, Pitts, Brandon,  and Sankaranarayanan, Sriram

   In 4th IFAC Workshop on Cyber-Physical Human-Systems Dec 2022

   Abs PDF Poster

   Recently, the use of artificial potential fields, known as risk fields, has been proposed for modeling human driver decision making. Such potential fields map from vehicle states and control inputs to a numerical risk measure such that the probability of choosing a control decreases as the risk associated increases. In this paper, we show that such a model can be used in a natural manner to also capture aspects of the driver’s situational awareness, assuming that the risk fields govern their underlying behavior. We demonstrate our ideas on a specific obstacle avoidance scenario wherein obstacles to be avoided are placed in front of a driver at predicable intervals. Using data collected on a pilot experiment involving six different drivers using a high-fidelity driving simulator, we demonstrate the ability of our approach to capture the likelihood that the driver has perceived/reacted to the obstacle. Our approach works for scenarios when the driver collides with the obstacle as well as scenarios involving successful collision avoidance.

4. HRI

   More Than a Number : A Multi-dimensional Framework For Automatically Assessing Human Teleoperation Skill

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

   In Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction Mar 2023

   Abs PDF Poster

   We present a framework for the formal evaluation of human teleoperator skill level in a systematic fashion, aiming to quantify how skillful a particular operator is for a well-defined task. Our proposed framework has two parts. First, the tasks used to evaluate skill levels are decomposed into a series of domain-specific primitives, each with a formal specification using signal temporal logic. Secondly, skill levels are automatically evaluated along multiple dimensions rather than a singular number. These dimensions include robustness, efficiency, resilience and readiness for each primitive task. We provide an initial evaluation for the task of taking-off, hovering, and landing in a drone simulator. This preliminary evaluation shows the value of a multi-dimensional evaluation of human operator performance.