The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content. Query-aware multi-video summarization is a promising technique that caters to this demand. In this work, we introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly optimizes multiple criteria: (1) conciseness, (2) representativeness of important query-relevant events and (3) chronological soundness. We design a hierarchical attention model that factorizes over three distributions, each collecting evidence from a different modality, followed by a pointer network that selects frames to include in the summary. DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence. We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.

Concepts are basic elements in any learning module and are thus very useful for modeling, summarizing, and previewing the content of any module. Automatic extraction of the major concepts from online education materials enables many useful applications. In this paper, we propose to leverage textbooks and their back-of-the-book indexes as training data to train a supervised machine learning algorithm for automatic extraction of concepts from text data in the education domain. We evaluate this idea by training neural networks on three textbooks and applying the trained neural networks to extract concepts from the lecture transcripts of two MOOCs. Our results suggest great promise for further exploration of this direction.

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.

Lecture slides covering many topics are becoming increasingly available online, but they are scattered, making it a challenge for anyone to instantly access all slides relevant to a learning context. To address this challenge, we propose to create links between those scattered slides to form a Web of Slides (WOS). Using the sequential nature of slides, we present preliminary results of studying how to automatically create a basic link based on similarity of slides as an initial step toward the vision of WOS. We also explore interesting future research directions using different link types and the unique features of slides.

We will demonstrate a prototype system WOSView built based on the vision of the Web of Slides(WOS), which aims to link all the lectures slides so as to facilitate navigation over all the slides. The links can be created at the slide level or at the level of phrases inside a slide, and many types of links can be created. The prototype system we built implements the most basic type of links, which link slides that have similar content and integrates lectures from four different MOOCs. WOSView also supports keyword search, which generates virtual links dynamically. We will demonstrate how the graphical interface of the WOSView enables students to flexibly navigate into slides from different courses and explore related slides using both static and dynamic links and solicit feedback from the community about the vision of WOS.

One of the main factors affecting the success and effectiveness of Massive Open Online Courses is the ability of the instructor to acquire and incorporate student feedback in a timely manner, and preferably before assigning grades to student assessments. This research uses raw clickstream data from video watching sessions of the Coursera MOOC: "Text Retrieval and Search Engines" to discover which topics are difficult for the students. We introduce a measure for topic difficulty based on these clickstream events, and rank the topics according to this measure. The validity of our ranking is evaluated by comparing it with the ranking of topics based on student votes and find that our method agrees with the ranking based on student votes with > 63% accuracy.