LearningAssistant: A novel learning resource recommendation system

Abstract

Reading online content for educational, learning, training or recreational purposes has become a very popular activity. While reading, people may have difficulty understanding a passage or wish to learn more about the topics covered by it, hence they may naturally seek additional or supplementary resources for the particular passage. These resources should be close to the passage both in terms of the subject matter and the reading level. However, using a search engine to find such resources interrupts the reading flow. It is also an inefficient, trial-and-error process because existing web search and recommendation systems do not support large queries, they do not understand semantic topics, and they do not take into account the reading level of the original document a person is reading. In this demo, we present LearningAssistant, a novel system that enables online reading material to be smoothly enriched with additional resources that can supplement or explain any passage from the original material for a reader on demand. The system facilitates the learning process by recommending learning resources (documents, videos, etc) for selected text passages of any length. The recommended resources are ranked based on two criteria (a) how they match the different topics covered within the selected passage, and (b) the reading level of the original text where the selected passage comes from. User feedback from students who use our system in two real pilots, one with a high school and one with a university, for their courses suggest that our system is promising and effective.

To print or not to print

“…All groups contained individuals from multiple institutions, teaching/majoring in a variety of subjects (with the exception of high school students). Students varied in age 15 , class year, and GPA. Each 8-person focus group participated in a 2 hour ssession (run by a contracted professional facilitator), consisting of a hands-on demonstration of METIS (with a discussion of features), followed by an in-depth round-table discussion to identify both sucessful features and areas for refinement.…”

“…When Hermione encounters a concept she doesn't understand, or wants to know more about, METIS leverages the technology from [15] to create an in-context search system (DR3). Hermione activates the hover menu and clicks the Search Icon (Figure 3f).…”

“…This allows her to take any selected text and get a set of search results from resources (configured by the Educator, Wikipedia, YouTube, or any other standardized website). The approach in [15] allows METIS, and thereby Hermione, to search on a high level concept dynamically extracted from a highlighted paragraph or full pages, providing more information without a Educator present. …”

Learning in the 21st century cyber-physical age

“…A key aspect of METIS is in allowing students to direct their own learning experience and to control the amount of mark-up to be added to the baseline materials. METIS incorporates technologies for document image retrieval (DIR), handwritten annotation extraction, layout, image recommendation, personalization, co-creation, and assessment [6][7][8][9]. These features facilitate and, in many cases, significantly simplify common teacher/student tasks.…”

Current and Alternate Approaches to Personalization in Online Learning

“…Creating and offering options that allow students to customize their learning paths based on the existing reading and language skill levels may ensure that they are using and engaging more with these online tools. A number of tools exist [8,12] that can be modified to support readability-driven assessments.…”