Continuous Evaluation of Video Lectures from Real-Time Difficulty Self-Report
Authors
With the increased reach and impact of video lectures, it is crucial to understand how they are experienced. Whereas previous studies typically present questionnaires at the end of the lecture, they fail to capture students' experience in enough granularity. In this paper we propose recording the lecture difficulty in real-time with a physical slider, enabling continuous and fine-grained analysis of the learning experience. We evaluated our approach in a study with 100 participants viewing two variants of two short lectures. We demonstrate that our approach helps us paint a more complete picture of the learning experience. Our analysis has design implications for instructors, providing them with a method that helps them compare their expectations with students' beliefs about the lectures and to better understand the specific effects of different instructional design decisions.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 100%
TSConnect: An Enhanced MOOC Platform for Bridging Communication Gaps Between Instructors and Students in Light of the Curse of Knowledge
IUI '25· Online Learning & MOOC Platforms +1
- 83%
VIVID: Human-AI Collaborative Authoring of Vicarious Dialogues from Lecture Videos
CHI '24· Human-LLM Collaboration +2
- 83%
TutorCraftEase: Enhancing Pedagogical Question Creation with Large Language Models
CHI '25· Human-LLM Collaboration +2
- 80%
Enhancing Online Problems Through Instructor-Centered Tools for Randomized Experiments
CHI '18· Online Learning & MOOC Platforms +1
- 80%
Reinforcement Learning for the Adaptive Scheduling of Educational Activities
CHI '20· Online Learning & MOOC Platforms +1
- 80%
Scripted Vicarious Dialogues: Educational Video Augmentation Method for Increasing Isolated Students’ Engagement
CHI '23· Online Learning & MOOC Platforms +1
- 67%
Time to Scale: Generalizable Affect Detection for Tens of Thousands of Students across An Entire School Year
CHI '19· Cognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia) +1
- 67%
QuizBot: A Dialogue-based Adaptive Learning System for Factual Knowledge
CHI '19· Conversational Chatbots +1
- 67%
Exploring the Potential of an Intelligent Tutoring System for Sketching Fundamentals
CHI '20· Programming Education & Computational Thinking +1
- 67%
Toward Automated Feedback on Teacher Discourse to Enhance Teacher Learning
CHI '20· Intelligent Tutoring Systems & Learning Analytics +1
Based on Jaccard similarity of research subtopics & professions (≥60%)