A Systematic Review on Process Mining for Curricular Analysis

• URL: http://arxiv.org/abs/2409.09204v1
• Date: Fri, 13 Sep 2024 21:35:11 GMT
• Title: A Systematic Review on Process Mining for Curricular Analysis
• Authors: Daniel Calegari, Andrea Delgado,
• Abstract summary: Educational Process Mining (EPM) is a data analysis technique that is used to improve educational processes. One specific application of EPM is curriculum mining, which focuses on understanding the learning program students follow to achieve educational goals. We conducted a systematic literature review to identify works on applying PM to curricular analysis and provide insights for further research.
• Score: 0.9208007322096533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Educational Process Mining (EPM) is a data analysis technique that is used to improve educational processes. It is based on Process Mining (PM), which involves gathering records (logs) of events to discover process models and analyze the data from a process-centric perspective. One specific application of EPM is curriculum mining, which focuses on understanding the learning program students follow to achieve educational goals. This is important for institutional curriculum decision-making and quality improvement. Therefore, academic institutions can benefit from organizing the existing techniques, capabilities, and limitations. We conducted a systematic literature review to identify works on applying PM to curricular analysis and provide insights for further research. From the analysis of 22 primary studies, we found that results can be classified into five categories concerning the objectives they pursue: the discovery of educational trajectories, the identification of deviations in the observed behavior of students, the analysis of bottlenecks, the analysis of stopout and dropout problems, and the generation of recommendation. Moreover, we identified some open challenges and opportunities, such as standardizing for replicating studies to perform cross-university curricular analysis and strengthening the connection between PM and data mining for improving curricular analysis.

Related papers

• VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning [47.81337826098964]
  We present VisAidMath, a benchmark for evaluating the problem-solving process related to visual information. This benchmark includes 1,200 challenging problems from various mathematical branches, vision-aid formulations, and difficulty levels. We conduct evaluations on ten mainstream LLMs and LMMs, highlighting deficiencies in the visual-aided reasoning process.
  arXiv  Detail & Related papers  (2024-10-30T13:19:44Z)
• A Systematic Review of Business Process Improvement: Achievements and Potentials in Combining Concepts from Operations Research and Business Process Management [0.0]
  Business Process Management and Operations Research aim to enhance value creation in organizations. This systematic literature review identifies and analyzes work that uses combined concepts from both disciplines. Results indicate a strong focus on resource allocation and scheduling problems.
  arXiv  Detail & Related papers  (2024-09-02T14:13:14Z)
• A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches) [4.499009117849108]
  We perform a systematic review of academic literature to investigate the integration of AI/ML in business process management. In business process management and process map, AI/ML has made significant improvements using operational data on process metrics.
  arXiv  Detail & Related papers  (2024-07-07T18:26:00Z)
• Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing [61.98556945939045]
  We propose a framework to learn planning-based reasoning through Direct Preference Optimization (DPO) on collected trajectories. Our results on challenging logical reasoning benchmarks demonstrate the effectiveness of our learning framework.
  arXiv  Detail & Related papers  (2024-02-01T15:18:33Z)
• Extracting Rules from Event Data for Study Planning [5.305245019481161]
  We employ process and data mining techniques to explore the impact of sequences of taken courses on academic success. The evaluation focuses on RWTH Aachen University computer science bachelor program students.
  arXiv  Detail & Related papers  (2023-10-04T11:14:51Z)
• Sequential pattern mining in educational data: The application context, potential, strengths, and limitations [3.680403821470857]
  Sequential pattern mining can be a valuable tool in educational data science. It can reveal unique insights about learning processes and be powerful for self-regulated learning research. Future research may improve its utility in educational data science by developing tools for counting pattern occurrences.
  arXiv  Detail & Related papers  (2023-02-03T06:56:31Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• Towards Automated Process Planning and Mining [77.34726150561087]
  We present a research project in which researchers from the AI and BPM field work jointly together. We discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models.
  arXiv  Detail & Related papers  (2022-08-18T16:41:22Z)
• Process-BERT: A Framework for Representation Learning on Educational Process Data [68.8204255655161]
  We propose a framework for learning representations of educational process data. Our framework consists of a pre-training step that uses BERT-type objectives to learn representations from sequential process data. We apply our framework to the 2019 nation's report card data mining competition dataset.
  arXiv  Detail & Related papers  (2022-04-28T16:07:28Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks [0.0]
  We develop a technique to determine the relevance scores for process activities with respect to performance measures. Annotating process models with such relevance scores facilitates a problem-focused analysis of the business process. We quantitatively evaluate the predictive quality of our technique using four datasets from different domains, to demonstrate the faithfulness of the relevance scores.
  arXiv  Detail & Related papers  (2020-08-07T12:15:30Z)

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.