3,494 research outputs found

    Providing behaviour awareness in collaborative project courses

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    Several studies show that awareness mechanisms can contribute to enhance the collaboration process among students and the learning experiences during collaborative project courses. However, it is not clear what awareness information should be provided to whom, when it should be provided, and how to obtain and represent such information in an accurate and understandable way. Regardless the research efforts done in this area, the problem remains open. By recognizing the diversity of work scenarios (contexts) where the collaboration may occur, this research proposes a behaviour awareness mechanism to support collaborative work in undergraduate project courses. Based on the authors previous experiences and the literature in the area, the proposed mechanism considers personal and social awareness components, which represent metrics in a visual way, helping students realize their performance, and lecturers intervene when needed. The trustworthiness of the mechanisms for determining the metrics was verified using empirical data, and the usability and usefulness of these metrics were evaluated with undergraduate students. Experimental results show that this awareness mechanism is useful, understandable and representative of the observed scenarios.Peer ReviewedPostprint (published version

    Providing behaviour awareness in collaborative project courses

    Get PDF
    Several studies show that awareness mechanisms can contribute to enhance the collaboration process among students and the learning experiences during collaborative project courses. However, it is not clear what awareness information should be provided to whom, when it should be provided, and how to obtain and represent such information in an accurate and understandable way. Regardless the research efforts done in this area, the problem remains open. By recognizing the diversity of work scenarios (contexts) where the collaboration may occur, this research proposes a behaviour awareness mechanism to support collaborative work in undergraduate project courses. Based on the authors previous experiences and the literature in the area, the proposed mechanism considers personal and social awareness components, which represent metrics in a visual way, helping students realize their performance, and lecturers intervene when needed. The trustworthiness of the mechanisms for determining the metrics was verified using empirical data, and the usability and usefulness of these metrics were evaluated with undergraduate students. Experimental results show that this awareness mechanism is useful, understandable and representative of the observed scenarios.Peer ReviewedPostprint (published version

    Functional Baby Talk: Analysis of Code Fragments from Novice Haskell Programmers

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    What kinds of mistakes are made by novice Haskell developers, as they learn about functional programming? Is it possible to analyze these errors in order to improve the pedagogy of Haskell? In 2016, we delivered a massive open online course which featured an interactive code evaluation environment. We captured and analyzed 161K interactions from learners. We report typical novice developer behavior; for instance, the mean time spent on an interactive tutorial is around eight minutes. Although our environment was restricted, we gain some understanding of Haskell novice errors. Parenthesis mismatches, lexical scoping errors and do block misunderstandings are common. Finally, we make recommendations about how such beginner code evaluation environments might be enhanced

    Harvey: A Greybox Fuzzer for Smart Contracts

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    We present Harvey, an industrial greybox fuzzer for smart contracts, which are programs managing accounts on a blockchain. Greybox fuzzing is a lightweight test-generation approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by narrow checks, which are satisfied by no more than a few input values. Moreover, most real-world smart contracts transition through many different states during their lifetime, e.g., for every bid in an auction. To explore these states and thereby detect deep vulnerabilities, a greybox fuzzer would need to generate sequences of contract transactions, e.g., by creating bids from multiple users, while at the same time keeping the search space and test suite tractable. In this experience paper, we explain how Harvey alleviates both challenges with two key fuzzing techniques and distill the main lessons learned. First, Harvey extends standard greybox fuzzing with a method for predicting new inputs that are more likely to cover new paths or reveal vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in a targeted and demand-driven way. We have evaluated our approach on 27 real-world contracts. Our experiments show that the underlying techniques significantly increase Harvey's effectiveness in achieving high coverage and detecting vulnerabilities, in most cases orders-of-magnitude faster; they also reveal new insights about contract code.Comment: arXiv admin note: substantial text overlap with arXiv:1807.0787

    TopicGPT: A Prompt-based Topic Modeling Framework

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    Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea leaves" to interpret; additionally, they offer users minimal semantic control over topics. To tackle these issues, we introduce TopicGPT, a prompt-based framework that uses large language models (LLMs) to uncover latent topics within a provided text collection. TopicGPT produces topics that align better with human categorizations compared to competing methods: for example, it achieves a harmonic mean purity of 0.74 against human-annotated Wikipedia topics compared to 0.64 for the strongest baseline. Its topics are also more interpretable, dispensing with ambiguous bags of words in favor of topics with natural language labels and associated free-form descriptions. Moreover, the framework is highly adaptable, allowing users to specify constraints and modify topics without the need for model retraining. TopicGPT can be further extended to hierarchical topical modeling, enabling users to explore topics at various levels of granularity. By streamlining access to high-quality and interpretable topics, TopicGPT represents a compelling, human-centered approach to topic modeling

    Toward Better Training in Peer Assessment: Does Calibration Help?

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    For peer assessments to be helpful, student reviewers need to submit reviews of good quality. This requires certain training or guidance from teaching staff, lest reviewers read each other\u27s work uncritically, and assign good scores but offer few suggestions. One approach to improving the review quality is calibration. Calibration refers to comparing students\u27 individual reviews to a standard—usually a review done by teaching staff on the same reviewed artifact. In this paper, we categorize two modes of calibration for peer assessment and discuss our experience with both of them in a pilot study with Expertiza system

    Towards Personalized Learning using Counterfactual Inference for Randomized Controlled Trials

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    Personalized learning considers that the causal effects of a studied learning intervention may differ for the individual student (e.g., maybe girls do better with video hints while boys do better with text hints). To evaluate a learning intervention inside ASSISTments, we run a randomized control trial (RCT) by randomly assigning students into either a control condition or a treatment condition. Making the inference about causal effects of studies interventions is a central problem. Counterfactual inference answers “What if� questions, such as Would this particular student benefit more if the student were given the video hint instead of the text hint when the student cannot solve a problem? . Counterfactual prediction provides a way to estimate the individual treatment effects and helps us to assign the students to a learning intervention which leads to a better learning. A variant of Michael Jordan\u27s Residual Transfer Networks was proposed for the counterfactual inference. The model first uses feed-forward neural networks to learn a balancing representation of students by minimizing the distance between the distributions of the control and the treated populations, and then adopts a residual block to estimate the individual treatment effect. Students in the RCT usually have done a number of problems prior to participating it. Each student has a sequence of actions (performance sequence). We proposed a pipeline to use the performance sequence to improve the performance of counterfactual inference. Since deep learning has achieved a huge amount of success in learning representations from raw logged data, student representations were learned by applying the sequence autoencoder to performance sequences. Then, incorporate these representations into the model for counterfactual inference. Empirical results showed that the representations learned from the sequence autoencoder improved the performance of counterfactual inference

    Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

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    The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

    Experience report on the use of technology to manage capstone course projects

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