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1. Domain Model Discovery from Textbooks for Computer Programming Intelligent Tutors

   https://doi.org/https://doi.org/10.32473/flairs.v34i1.128561  

   Banjade, R. (January 2021, he International FLAIRS Conference Proceedings, 34.)

   null (Ed.)

   We present a novel approach to intro-to-programming domain model discovery from textbooks using an over-generation and ranking strategy. We first extract candidate key phrases from each chapter in a Computer Science textbook focusing on intro-to-programming and then rank those concepts according to a number of metrics such as the standard tf-idf weight used in information retrieval and metrics produced by other text ranking algorithms. Specifically, we conduct our work in the context of developing an intelligent tutoring system for source code comprehension for which a specification of the key programming concepts is needed - the system monitors students' performance on those concepts and scaffolds their learning process until they show mastery of the concepts. Our experiments with programming concept instruction from Java textbooks indicate that the statistical methods such as KP Miner method are quite competitive compared to other more sophisticated methods. Automated discovery of domain models will lead to more scalable Intelligent Tutoring Systems (ITSs) across topics and domains, which is a major challenge that needs to be addressed if ITSs are to be widely used by millions of learners across many domains. 

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2. OpenTutor: Designing a Rapid-Authored Tutor that Learns as you Grade

   https://doi.org/10.32473/flairs.v34i1.128576  

   Nye, Benjamin D; Sanghrajka, Rushit; Bodhwani, Vinit; Acob, Martin; Budziwojski, Daniel; Carr, Kayla; Kirshner, Larry; Swartout, William R (May 2021, The International FLAIRS Conference Proceedings)

   Despite strong evidence that dialog-based intelligent tutoring systems (ITS) can increase learning gains, few courses include these tutors. In this research, we posit that existing dialog-based tutoring systems are not widely used because they are too complex and unfamiliar for a typical teacher to adapt or augment. OpenTutor is an open-source research project intended to scale up dialog-based tutoring by enabling ordinary teachers to rapidly author and improve dialog-based ITS, where authoring is presented through familiar tasks such as assessment item creation and grading. Formative usability results from a set of five non-CS educators are presented, which indicate that the OpenTutor system was relatively easy to use but that teachers would closely consider the cost benefit for time vs. student outcomes. Specifically, while OpenTutor grading was faster than expected, teachers reported that they would only spend any additional time (compared to a multiple choice) if the content required deeper learning. To decrease time to train answer classifiers, OpenTutor is investigating ways to reduce cold-start problems for tutoring dialogs. 

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3. What Time is It? Student Modeling Needs to Know.

   Mao, Y.; Marwan, S.; Price, T.W.; Barnes, T.; & Chi, M. (January 2020, In Proceedings of the 13th International Conference on Educational Data Mining (EDM) 2020)

   null (Ed.)

   Abstract: Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and classic Markovian models such as Bayesian Knowledge Tracing (BKT) have been successfully applied for student modeling. However, much of this prior work is designed to handle sequences of events with discrete timesteps, rather than considering the continuous aspect of time. Given that time elapsed between successive elements in a student’s trajectory can vary from seconds to days, we applied a Timeaware LSTM (T-LSTM) to model the dynamics of student knowledge state in continuous time. We investigate the effectiveness of T-LSTM on two domains with very different characteristics. One involves an open-ended programming environment where students can self-pace their progress and T-LSTM is compared against LSTM, Recent Temporal Pattern Mining, and the classic Logistic Regression (LR) on the early prediction of student success; the other involves a classic tutor-driven intelligent tutoring system where the tutor scaffolds the student learning step by step and T-LSTM is compared with LSTM, LR, and BKT on the early prediction of student learning gains. Our results show that TLSTM significantly outperforms the other methods on the self-paced, open-ended programming environment; while on the tutor-driven ITS, it ties with LSTM and outperforms both LR and BKT. In other words, while time-irregularity exists in both datasets, T-LSTM works significantly better than other student models when the pace is driven by students. On the other hand, when such irregularity results from the tutor, T-LSTM was not superior to other models but its performance was not hurt either. 

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4. Automated Extraction of Domain Models from Textbook Indexes for Developing Intelligent Tutoring Systems

   https://doi.org/10.1007/978-3-031-32883-1_11  

   Banjade, R.; Oli, P.; Rus, V. (May 2023, Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891)

   Frasson, C.; Mylonas, P.; Troussas, C. (Ed.)

   Domain modeling is an important task in designing, developing, and deploying intelligent tutoring systems and other adaptive instructional systems. We focus here on the more specific task of automatically extracting a domain model from textbooks. In particular, this paper explores using multiple textbook indexes to extract a domain model for computer programming. Our approach is based on the observation that different experts, i.e., authors of intro-to-programming textbooks in our case, break down a domain in slightly different ways, and identifying the commonalities and differences can be very revealing. To this end, we present automated approaches to extracting domain models from multiple textbooks and compare the resulting common domain model with a domain model created by experts. Specifically, we use approximate string-matching approaches to increase coverage of the resulting domain model and majority voting across different textbooks to discover common domain terms related to computer programming. Our results indicate that using approximate string matching gives more accurate domain models for computer programming with increased precision and recall. By automating our approach, we can significantly reduce the time and effort required to construct high-quality domain models, making it easy to develop and deploy tutoring systems. Furthermore, we obtain a common domain model that can serve as a benchmark or skeleton that can be used broadly and adapted to specific needs by others. 

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5. ScrapeViz: Hierarchical Representations for Web Scraping Macros

   https://doi.org/10.1109/VL/HCC60511.2024.00040  

   Krosnick, Rebecca; Oney, Steve (September 2024, IEEE)

   Programming-by-demonstration (PBD) makes it possible to create web scraping macros without writing code. However, it can still be challenging for users to understand the exact scraping behavior that is inferred and to verify that the scraped data is correct, especially when scraping occurs across multiple pages. We present ScrapeViz, a new PBD tool for authoring and visualizing hierarchical web scraping macros. ScrapeViz’s key novelty is in providing a visual representation of web scraping macros-the sequences of pages visited, generalized scraping behavior across similar pages, and data provenance. We conducted a lab study with 12 participants comparing ScrapeViz to the existing web scraping tool Rousillon and saw that participants found ScrapeViz helpful for understanding high-level scraping behavior, tracing the source of scraped data, identifying anomalies, and validating macros while authoring. 

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