Online educational videos have the potential to enhance undergraduate biology learning, for example by showcasing contemporary scientific research and providing content coverage. Here, we describe the integration of nine videos into a large‐enrollment (
This content will become publicly available on June 12, 2025
Understanding Embodied Robotics Learning Using Video Based LLM Methods
In this talk, we have presented the use of LLM to study the embodied learning. We have discovered that the use of LLM is really effective to summarize the videos of embodied learning and conventional learning. LLM is also very effective in summarize the sentiment of the user comments of the two different videos. Correlation between the user comments and video content was also made. It is discovered that the use of the embodied learning is rather effective to engage learners in learning robotics
more »
« less
- Award ID(s):
- 2306285
- PAR ID:
- 10542577
- Publisher / Repository:
- eCOTS 2024 Regional Conference
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract n = 356) introductory evolution and ecology course via weekly homework assignments. We predicted that videos that feature research stories from contemporary scientists could reinforce topics introduced in lecture and provide students with novel insights into the nature of scientific research. Using qualitative analysis of open‐ended written feedback from the students on each video assigned throughout the term (n = 133–229 responses per video) and on end‐of‐quarter evaluations (n = 243), we identified common categories of student perspectives. All videos received more positive than negative comments and all videos received comments indicating that students found them intellectually and emotionally stimulating, accessible, and relevant to course content. Additionally, all videos also received comments indicating some students found them intellectually unstimulating, though these comments were generally far less numerous than positive comments. Students responded positively to videos that incorporated at least one of the following: documentary‐style filming, very clear links to course content (especially hands‐on activities completed by the students), relevance to recent world events, clarity on difficult topics, and/or charismatic narrators or species. We discuss opportunities and challenges for the use of online educational videos in teaching ecology and evolution, and we provide guidelines instructors can use to integrate them into their courses. -
Budak, Ceren ; Cha, Meeyoung ; Quercia, Daniele ; Xie, Lexing (Ed.)We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf.more » « less
-
We study interactive learning of LLM-based language agents based on user edits made to the agent's output. In a typical setting such as writing assistants, the user interacts with a language agent to generate a response given a context, and may optionally edit the agent response to personalize it based on their latent preference, in addition to improving the correctness. The edit feedback is naturally generated, making it a suitable candidate for improving the agent's alignment with the user's preference, and for reducing the cost of user edits over time. We propose a learning framework, PRELUDE that infers a description of the user's latent preference based on historic edit data. The inferred user preference descriptions are used to define prompts for generating responses in the future. This avoids fine-tuning the agent, which is costly, challenging to scale with the number of users, and may even degrade its performance on other tasks. Furthermore, learning descriptive preference improves interpretability, allowing the user to view and modify the learned preference. However, user preference can be complex, subtle, and vary based on context, making it challenging to learn. To address this, we propose a simple yet effective algorithm named CIPHER that leverages the LLM to infer the user preference for a given context based on user edits. In the future, CIPHER retrieves inferred preferences from the k-closest contexts in the history, and forms an aggregate preference for response generation. We introduce two interactive environments -- summarization and email writing, and use a GPT-4 simulated user for evaluation. On both tasks, CIPHER outperforms several baselines by achieving the lowest edit distance cost while only having a small overhead in LLM query cost. Our analysis reports that user preferences learned by CIPHER show significant similarity to the ground truth latent preferences.more » « less
-
Feedback is a crucial factor in mathematics learning and instruction. Whether expressed as indicators of correctness or textual comments, feedback can help guide students’ understanding of content. Beyond this, however, teacher-written messages and comments can provide motivational and affective benefits for students. The question emerges as to what constitutes effective feedback to promote not only student learning but also motivation and engagement. Teachers may have different perceptions of what constitutes effective feedback utilizing different tones in their writing to communicate their sentiment while assessing student work. This study aims to investigate trends in teacher sentiment and tone when providing feedback to students in a middle school mathematics class context. Toward this, we examine the applicability of state-of-the-art sentiment analysis methods in a mathematics context and explore the use of punctuation marks in teacher feedback messages as a measure of tone.more » « less
-
Feedback is a crucial factor in mathematics learning and instruction. Whether expressed as indicators of correctness or textual comments, feedback can help guide students’ understanding of content. Beyond this, however, teacher-written messages and comments can provide motivational and affective benefits for students. The question emerges as to what constitutes effective feedback to promote not only student learning but also motivation and engagement. Teachers may have different perceptions of what constitutes effective feedback utilizing different tones in their writing to communicate their sentiment while assessing student work. This study aims to investigate trends in teacher sentiment and tone when providing feedback to students in a middle school mathematics class context. Toward this, we examine the applicability of state-of-the-art sentiment analysis methods in a mathematics context and explore the use of punctuation marks in teacher feedback messages as a measure of tone.more » « less
