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Textual analogies that make comparisons between two concepts are often used for explaining complex ideas, creative writing, and scientific discovery. In this paper, we propose and study a new task, called Analogy Detection and Extraction (AnaDE), which includes three synergistic sub-tasks: 1) detecting documents containing analogies, 2) extracting text segments that make up the analogy, and 3) identifying the (source and target) concepts being compared. To facilitate the study of this new task, we create a benchmark dataset by scraping Metamia.com and investigate the performances of state-of-the-art models on all sub-tasks to establish the first-generation benchmark results for this new task. We find that the Longformer model achieves the best performance on all the three sub-tasks demonstrating its effectiveness for handling long texts. Moreover, smaller models fine-tuned on our dataset perform better than non-finetuned ChatGPT, suggesting high task difficulty. Overall, the models achieve a high performance on documents detection suggesting that it could be used to develop applications like analogy search engines. Further, there is a large room for improvement on the segment and concept extraction tasks. 

Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively. In this work, we conducted a user study where 58 learners (in a course of 387 learners) participated in the editing of captions in 89 lecture videos that were generated by Automatic Speech Recognition (ASR) technologies. For each video, different learners conducted two rounds of editing. Based on editing logs, we created a taxonomy of errors in educational video captions (e.g., Discipline-Specific, General, Equations). From the interviews, we identified individual and collaborative error editing strategies. We then further demonstrated the feasibility of applying machine learning models to assist learners in editing. Our work provides practical implications for advancing video-based learning and for educational video caption editing. 

Many universities are offering data science (DS) courses to fulfill the growing demands for skilled DS practitioners. Assignments and projects are essential parts of the DS curriculum as they enable students to gain hands-on experience in real-world DS tasks. However, most current assignments and projects are lacking in at least one of two ways: 1) they do not comprehensively teach all the steps involved in the complete workflow of DS projects; 2) students work on separate problems individually or in small teams, limiting the scale and impact of their solutions. To overcome these limitations, we envision novel synergistic modular assignments where a large number of students work collectively on all the tasks required to develop a large-scale DS product. The resulting product can be continuously improved with students' contributions every semester. We report our experience with developing and deploying such an assignment in an Information Retrieval course. Through the assignment, students collectively developed a search engine for finding expert faculty specializing in a given field. This shows the utility of such assignments both for teaching useful DS skills and driving innovation and research. We share useful lessons for other instructors to adopt similar assignments for their DS courses.