Transforming Textbooks into Learning by Doing Environments: An Evaluation of Textbook-Based Automatic Question Generation
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This document evaluates the use of automatically generated questions in textbooks to promote active learning. It finds that engagement with automatically generated questions was similar to human-authored questions and not deterred. Difficulty levels of automatically generated questions also fell within the range of human-authored questions. Placement of questions in the course and whether they assessed recall or recognition impacted engagement. Very difficult questions reduced student persistence but easy questions did not. The evaluation suggests that automatically generated questions can effectively promote active learning without deterring students.
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The Doer Effect and the AI Solution
The Doer Effect is the learning science principle that doing
formative practice while reading has a six times larger effect size
on learning outcomes than reading alone, a relationship that has
been shown to be causal [10–12].
Formative practice is good for students, but costly to create.
Automatic question generation can solve this problem.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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As textbooks evolve into digital platforms, they open a world of opportunities for Artificial Intelligence in Education (AIED) research. This paper delves into the novel use of textbooks as a source of high-quality labeled data for automatic keyword extraction, demonstrating an affordable and efficient alternative to traditional methods. By utilizing the wealth of structured information provided in textbooks, we propose a methodology for annotating corpora across diverse domains, circumventing the costly and time-consuming process of manual data annotation. Our research presents a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) fine-tuned on this newly labeled dataset. This model is applied to keyword extraction tasks, with the model’s performance surpassing established baselines. We further analyze the transformation of BERT’s embedding space before and after the fine-tuning phase, illuminating how the model adapts to specific domain goals. Our findings substantiate textbooks as a resource-rich, untapped well of high-quality labeled data, underpinning their significant role in the AIED research landscape.
Toward Eliminating Hallucinations: GPT-based Explanatory AI for Intelligent T...
Traditional explanatory resources, such as user manuals and textbooks, often contain content that may not cater to the diverse backgrounds and information needs of users. Yet, developing intuitive, user-centered methods to effectively explain complex or large amounts of information is still an open research challenge. In this paper we present ExplanatoryGPT, an approach we devised and implemented to transform textual documents into interactive, intelligent resources, capable of offering dynamic, personalized explanations. Our approach uses state-of-the-art question-answering technology to generate on-demand, expandable explanations, with the aim of allowing readers to efficiently navigate and comprehend static materials. ExplanatoryGPT integrates ChatGPT, a state-of-the-art language model, with Achinstein’s philosophical theory of explanations. By combining question generation and answer retrieval algorithms with ChatGPT, our method generates interactive, user-centered explanations, while mitigating common issues associated with ChatGPT, such as hallucinations and memory shortcomings. To showcase the effectiveness of our Explanatory AI, we conducted tests using a variety of sources, including a legal textbook and documentation of some health and financial software. Specifically, we provide several examples that illustrate how ExplanatoryGPT excels over ChatGPT in generating more precise explanations, accomplished through thoughtful macro-planning of explanation content. Notably, our approach also avoids the need to provide the entire context of the explanation as a prompt to ChatGPT, a process that is often not feasible due to common memory constraints.
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Ensuring accessible textbooks for children with disabilities is essential for inclusive education. However, providing native accessibility for educational content remains a challenge. In the mean time, existing educational materials need to be adapted, for example by providing interactive versions to overcome difficulties caused by disabilities. In this context, our project aims to automatically adapt PDF textbooks to make them accessible to children with disabilities. The first step towards this adaptation involves extracting and structuring the content of textbooks. In this paper, we introduce textbook models, propose an automated extraction pipeline, and conduct preliminary experiments. Our textbook models are based on the various activities involved and provide layout and semantic information. They enable normalized and structured representations of educational content at both document and page levels, facilitating the automatic extraction process and the conversion to popular formats such as TEI and DocBook. In order to automatically extract PDF textbooks structure, our experiments, using a state-of-the-art multimodal transformer for a token classification task, demonstrate promising results. However, these experiments also highlight the difficulty of the task, especially cross-textbook collection generalization. Finally, we discuss the extraction pipeline and the directions of future work.
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The content ecosystem is an often overlooked component of adaptive tutors and intelligent tutoring systems. A robust and scalable content environment and creation process is critical in sustaining the long-term usage and subject area expansion of a tutor. Open Adaptive Tutor (OATutor), a recently introduced open-source adaptive tutor, produced three textbooks worth of creative commons content with custom built hints and scaffolds and problems sourced from OpenStax. In this paper, we examine how OATutor facilitated the creation of its content over a three-year period, as well as explore its content ecosystem, including the creation process and content team structure. We argue that the created content falls into the realm of intelligent textbooks and can be incorporated into digital textbooks to make them interactive.
Creating Session Data from eTextbook Event Streams
As more students interact with online learning platforms and eTextbooks, they generate massive amounts of data. For example, the OpenDSA eTextbook system collects clickstream data as students interact with prose, visualizations, and interactive, auto-graded exercises. A large class with a lot of system usage could easily generate in excess of a million events during a semester. Ideally, instructors and system developers can harness this information to create better instructional experiences. But in its raw event-level form, it is difficult for developers or instructors to understand student behaviors, or to make testable hypotheses about relationships between behavior and performance. In this paper, we describe our efforts to break raw event-level clickstreams first into sessions (a continuous series of work by a student) and then to meaningfully abstract the events into higher-level descriptions of that session. We first bundle events into related activities, such as the events associated with stepping through a given visualization, or with working a given exercise. Each such group of events defines a state. A state is a basic unit that can characterize the interaction log data, and there are multiple types including reading, slideshows, and exercises state. We hope that this process of creating semantic-level abstractions of work sessions can be a useful step in the analysis pipeline to give us better insight into students’ learning behaviors.
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A powerful set of educational tools has emerged over the last decade with the rise in the adoption of online adaptive learning content. An increasingly popular tool in this space is the “intelligent textbook” as a platform to support and distribute content for e-learning, given its re- semblance with real-life physical books. Existing efforts in this direction include the development of digital textbooks where both textual con- tent and interactive learning activities (i.e., examples, problems, etc.) are carefully handcrafted by the authors so that they are perfectly placed to follow the knowledge acquisition-practice flow. However, this approach is very time-consuming, and it requires the work of high-expertise authors. In this work, we suggest and discuss a scalable solution: we take existing digital textbooks and augment them by using repositories of existing on- line learning material associated with the subject matter. We present our current work in this direction and discuss challenges and opportunities for the future work.
Interactions of reading and assessment activities
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Adding Intelligence to a Textbook for Human Anatomy with a Causal Concept Map...
This document describes adding intelligence to a human anatomy textbook through a causal concept map based intelligent tutoring system (ITS). The ITS aims to address three main challenges students face in learning anatomy: 1) integrating information, 2) understanding simultaneously occurring processes, and 3) building a hierarchy of anatomical relationships. The ITS would include a causal concept map to represent relationships between concepts, practice test questions to assess understanding, and search/retrieval capabilities to look up concepts in the textbook PDF. The goal is to help students better understand anatomical functions, causes and effects, and which components control other components through interactive learning with the ITS.
Interlingua: Linking Textbooks Across Different Languages
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Transforming Textbooks into Learning by Doing Environments: An Evaluation of Textbook-Based Automatic Question Generation
- 1. c Transforming Textbooks into Learning by Doing Environments: An Evaluation of Textbook-Based Automatic Question Generation
- 2. c The Doer Effect and the AI Solution The Doer Effect is the learning science principle that doing formative practice while reading has a six times larger effect size on learning outcomes than reading alone, a relationship that has been shown to be causal [10–12]. Formative practice is good for students, but costly to create. Automatic question generation can solve this problem.
- 3. c The State of Research In Kurdi et al.’s 2020 systematic review of research, there are gaps identified in the current research: • Only 1 study evaluated AG questions in a classroom setting • Only 1 study generated feedback • Only 1 study identified Bloom’s level • Only 14 studies evaluated question difficulty • There is no clear “gold standard” identified
- 5. c The Data: 786,242 total observations
- 6. c Evaluating Questions We compare our AG questions to HA questions in the same course using a mixed-effects logistic regression model. In the same course, how do our AG questions compare to HA questions on: • Engagement • Difficulty • Persistence
- 7. c Engagement
- 8. c Engagement • For all questions, the location of the question in the course mattered. • The recognition question types had higher engagement than the recall question types. • The AG recognition type is similar to HA recognition types, and the AG recall type is similar to the HA recall type. • There is no indication that students found the AG question types problematic in general and chose to answer them less frequently. To summarize the engagement analysis:
- 9. c Difficulty
- 10. c Difficulty To summarize the difficulty analysis: • AQ matching were often the the easiest, but similar to other HA recognition types. • AG FITB were similar to several other HA questions, which were recognition types. • HA FITB were often marginally to significantly more difficult than AG FITB. • Questions varied in difficulty across courses, showing the impact of content.
- 11. c Persistence To summarize the persistence analysis: • AG FITB had statistically different persistence from all questions except HA MCMS. • HA FITB generally had lower persistence than AG FITB. • AG matching had similar persistence to other HA questions.
- 12. c In Summary Students were not deterred by AG questions. Engagement was impacted by placement in the course, and recognition vs recall types. AG questions were in the difficulty range of the HA questions. Easy questions did not deter persistence, but very difficult questions did.
- 13. c Thank You! If you have any further questions, please contact us: • Benny.Johnson@vitalsource.com • Rachel.Vancampenhout@vitalsource.com • Bill.Jerome@vitalsource.com
Editor's Notes
- Define these metrics