Mining educational data to provide intelligent information and personalized recommendations

挖掘教育数据，提供智能信息和个性化推荐

• 批准号: RGPIN-2020-05837
• 负责人: Graf, Sabine
• 金额: $ 2.99万
• 依托单位: Athabasca University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750407
• 关键词: Mining; educational; data; provide; intelligent

Online courses have become very popular in recent years. While such courses have many advantages, they also have drawbacks, including high dropout rates, little or no feedback for educators on how learners learn, and little or no personalization for learners. Other fields such as business have successfully used data analytics, data mining, user modelling and personalization to address similar problems but educational systems currently do not employ such techniques. The primary objective of this research program is to advance research in data analytics, data mining, user modelling, and personalization in the educational domain through designing and evaluating algorithms and techniques to analyze and mine educational data, create user profiles, and use these profiles to provide users (learners and educators) with personalized information and personalized recommendations for improvement. This research program aims to create and evaluate educational data mining algorithms and learning analytics techniques that can not only identify effective and ineffective behaviour patterns of learners and educators but also identify learners who are at risk of dropping out or failing a course. These algorithms and techniques will then be enriched with approaches from the fields of personalization and user modelling to advance current research and create truly personal profiles of learners and educators. At Athabasca University, I have access to data gathered from over 850 online courses - a dataset that is majestic in size and that can be used to produce reliable and generalizable results. Furthermore, this program aims to develop and evaluate artificial intelligence and recommender system techniques that will use the information generated about learners' risk levels as well as effective and ineffective behaviour patterns of learners and educators to provide personalized recommendations. Personalization will be applied on various levels, and an automatic feedback mechanism will be developed to further enhance the accuracy and usefulness of the recommendations. The final result will be an open-source tool suite consisting of a set of software solutions for learners and educators that can not only be integrated with common online learning systems but also used and built upon by other researchers. These software solutions will act as personal coaches for the learners and educators, giving them information about their learning and teaching processes (e.g., learner risk levels and the effectiveness of learner and educator behaviour patterns). These coaches will also provide personalized recommendations on how to change behaviour patterns to (a) improve the effectiveness of teaching and learning, (b) reduce dropout rates, and (c) improve outcomes for the learners. In addition, this program will demonstrate the potential of using data mining, data analytics, user modelling, and personalization to improve online learning systems and to benefit education across Canada.

近年来，在线课程变得非常流行。虽然这些课程有很多优点，但它们也有缺点，包括辍学率高，教育工作者对学习者如何学习的反馈很少或根本没有，学习者的个性化很少或根本没有。商业等其他领域已经成功地使用数据分析、数据挖掘、用户建模和个性化来解决类似的问题，但教育系统目前还没有使用这些技术。该研究计划的主要目标是通过设计和评估算法和技术来分析和挖掘教育数据，创建用户档案，并使用这些档案为用户（学习者和教育者）提供个性化信息和个性化改进建议，从而推进教育领域的数据分析、数据挖掘、用户建模和个性化研究。该研究项目旨在创建和评估教育数据挖掘算法和学习分析技术，不仅可以识别学习者和教育者的有效和无效行为模式，还可以识别有辍学或不及格风险的学习者。然后，这些算法和技术将通过个性化和用户建模领域的方法来丰富，以推进当前的研究，并创建学习者和教育者的真正个人档案。在阿萨巴斯卡大学（Athabasca University），我可以从850多门在线课程中收集数据——这是一个庞大的数据集，可以用来产生可靠的、可推广的结果。此外，该项目旨在开发和评估人工智能和推荐系统技术，这些技术将使用有关学习者风险水平以及学习者和教育者有效和无效行为模式的信息来提供个性化推荐。将在各级实行个性化，并将制定自动反馈机制，以进一步提高建议的准确性和有用性。最终的结果将是一个开源工具套件，由一套为学习者和教育者提供的软件解决方案组成，不仅可以与普通的在线学习系统集成，还可以被其他研究人员使用和构建。这些软件解决方案将充当学习者和教育者的私人教练，为他们提供有关学习和教学过程的信息（例如，学习者风险水平以及学习者和教育者行为模式的有效性）。这些教练还将就如何改变行为模式提供个性化建议，以(a)提高教与学的效率，(b)降低辍学率，(c)改善学习者的成果。此外，该项目将展示使用数据挖掘、数据分析、用户建模和个性化的潜力，以改善在线学习系统，并使加拿大的教育受益。