Example-Based Learning in Software Engineering Education: A Systematic Mapping Study

• URL: http://arxiv.org/abs/2503.18080v1
• Date: Sun, 23 Mar 2025 14:14:25 GMT
• Title: Example-Based Learning in Software Engineering Education: A Systematic Mapping Study
• Authors: Tiago P. Bonetti, Williamson Silva, Thelma E. Colanzi,
• Abstract summary: Example-Based Learning (EBL) has shown promise in improving the quality of Software Engineering Education (SEE)<n>This study aims to investigate and classify the existing empirical evidence about using EBL in SEE.
• Score: 0.43012765978447565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The discipline of Software Engineering (SE) allows students to understand specific concepts or problems while designing software. Empowering students with the necessary knowledge and skills for the software industry is challenging for universities. One key problem is that traditional methodologies often leave students as passive agents, limiting engagement and learning effectiveness. To address this issue, instructors must promote active learning in the classroom. Among the teaching methodologies, Example-Based Learning (EBL) has shown promise in improving the quality of Software Engineering Education (SEE). This study aims to investigate and classify the existing empirical evidence about using EBL in SEE. We carried out a systematic mapping to collect existing studies and evidence that describe how instructors have been employing EBL to teach SE concepts. By analyzing 30 studies, we identified the benefits and difficulties of using EBL, the SE contents taught by instructors, and the artifacts that support the methodology's use in the classroom. Besides, we identified the main types of examples used in SEE through EBL. We realized that EBL contributes to student learning, helping in students' interaction, interpreting and applying concepts, and increasing student motivation and confidence. However, some barriers to adopting EBL in SEE are increasing the effort required by instructors, lack of adequate learning support, and time spent constructing diagrams with errors. Overall, our findings suggest that EBL can improve the effectiveness of SEE, but more research is needed to address the gaps and challenges identified in our study.

Related papers

• INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models [0.7499722271664147]
  INSIGHT is a proof of concept to combine various AI tools to assist teaching staff and students in the process of solving exercises. We analyze students' questions to an LLM by extracting keywords, which we use to dynamically build an FAQ from students' questions.
  arXiv  Detail & Related papers  (2025-04-24T15:47:20Z)
• How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments? [0.8999666725996978]
  This study introduces an approach that integrates dynamic knowledge graphs with large language models (LLMs) to offer nuanced student assistance.<n>Central to this method is the knowledge graph's role in assessing a student's comprehension of topic prerequisites.<n>Preliminary findings suggest students could benefit from this tiered support, achieving enhanced comprehension and improved task outcomes.
  arXiv  Detail & Related papers  (2024-12-05T04:05:43Z)
• Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
  We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring. We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue. We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• Explainable Few-shot Knowledge Tracing [48.877979333221326]
  We propose a cognition-guided framework that can track the student knowledge from a few student records while providing natural language explanations. Experimental results from three widely used datasets show that LLMs can perform comparable or superior to competitive deep knowledge tracing methods.
  arXiv  Detail & Related papers  (2024-05-23T10:07:21Z)
• Evaluating and Optimizing Educational Content with Large Language Model Judgments [52.33701672559594]
  We use Language Models (LMs) as educational experts to assess the impact of various instructions on learning outcomes. We introduce an instruction optimization approach in which one LM generates instructional materials using the judgments of another LM as a reward function. Human teachers' evaluations of these LM-generated worksheets show a significant alignment between the LM judgments and human teacher preferences.
  arXiv  Detail & Related papers  (2024-03-05T09:09:15Z)
• YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
  This paper introduces YODA, a teacher-student progressive learning framework. It emulates the teacher-student education process to improve the efficacy of model fine-tuning. Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
  arXiv  Detail & Related papers  (2024-01-28T14:32:15Z)
• Knowledge Tracing Challenge: Optimal Activity Sequencing for Students [0.9814642627359286]
  Knowledge tracing is a method used in education to assess and track the acquisition of knowledge by individual learners. We will present the results of the implementation of two Knowledge Tracing algorithms on a newly released dataset as part of the AAAI2023 Global Knowledge Tracing Challenge.
  arXiv  Detail & Related papers  (2023-11-13T16:28:34Z)
• A Machine Learning system to monitor student progress in educational institutes [0.0]
  We propose a data driven approach that makes use of Machine Learning techniques to generate a classifier called credit score. The proposal to use credit score as progress indicator is well suited to be used in a Learning Management System.
  arXiv  Detail & Related papers  (2022-11-02T08:24:08Z)
• Canvas Adoption Assessment and Acceptance of the Learning Management System on a Web-Based Platform [0.0]
  This study aims to assess student adoption of Canvas as a new learning management system and its potential as a web-based platform in the e-learning programme of the University of the East. The students perceived ease of use has a significant effect on their perceived usefulness but has no significant effects on their attitude towards the use of Canvas.
  arXiv  Detail & Related papers  (2021-01-29T01:30:55Z)
• Dual Policy Distillation [58.43610940026261]
  Policy distillation, which transfers a teacher policy to a student policy, has achieved great success in challenging tasks of deep reinforcement learning. In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment. The key challenge in developing this dual learning framework is to identify the beneficial knowledge from the peer learner for contemporary learning-based reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2020-06-07T06:49:47Z)
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey [53.73359052511171]
  Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. We present a framework for curriculum learning (CL) in RL, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals.
  arXiv  Detail & Related papers  (2020-03-10T20:41:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.