Mining educational data to provide intelligent information and personalized recommendations

近年来，在线课程变得非常受欢迎。虽然这类课程有很多优点，但它们也有缺点，包括高辍学率，教育工作者很少或根本没有就学习者的学习方式提供反馈，以及很少或根本没有为学习者提供个性化服务。其他领域，如商业，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问题，但教育系统目前没有采用这种技术。 该研究计划的主要目标是通过设计和评估分析和挖掘教育数据的算法和技术，创建用户配置文件，并使用这些配置文件为用户(学习者和教育工作者)提供个性化信息和个性化改进建议，来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。 这项研究计划旨在创建和评估教育数据挖掘算法和学习分析技术，这些算法和学习分析技术不仅可以识别学习者和教育工作者的有效和无效行为模式，还可以识别面临辍学或失败风险的学习者。然后，这些算法和技术将被来自个性化和用户建模领域的方法丰富，以推进当前的研究并创建真正的学习者和教育者的个人档案。在阿萨巴斯卡大学，我可以访问从850多门在线课程中收集的数据，这些数据集规模巨大，可以用来产生可靠和可推广的结果。此外，该项目旨在开发和评估人工智能和推荐系统技术，这些技术将使用生成的关于学习者风险水平的信息以及学习者和教育工作者的有效和无效行为模式来提供个性化建议。将在各个层面应用个性化，并将开发自动反馈机制，以进一步提高建议的准确性和实用性。 最终结果将是一个开放源码工具套件，其中包括一套供学习者和教育工作者使用的软件解决方案，这些解决方案不仅可以与常见的在线学习系统集成，还可以供其他研究人员使用和构建。这些软件解决方案将作为学习者和教育工作者的个人教练，向他们提供有关其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工作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)提高教与学的有效性，(B)降低辍学率，(C)改善学习者的结果。此外，该项目将展示使用数据挖掘、数据分析、用户建模和个性化来改进在线学习系统并使加拿大各地的教育受益的潜力。

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.