Enhancing Teaching and Learning through Educational Data Min(3)

美国教育部教育数据挖掘概述文档

What are the challenges and barriers to successful application of educational data mining and learning analytics?

What new practices have to be adopted in order to successfully employ educational data mining and learning analytics for improving teaching and learning?

literature (Web pages and unpublished documents) on

educational data mining and learning analytics;

Interviews of 15 data mining/analytics experts from

learning software and learning management system

companies and from companies offering other kinds

of Web-based services; and

Deliberations of a technical working group of eight

academic experts in data mining and learning

analytics. Learning management systems (LMS) LMS are suites of software tools that provide comprehensive course-delivery functions—administration, documentation, content assembly and delivery, tracking and reporting of progress, user management and self-services, etc. LMS are Web based and are considered a platform on which to build and deliver modules and courses. Open-source examples include Moodle, Sakai, and ILIAS.

This issue brief was inspired by the vision of personalized learning and embedded assessment in the U.S. Department of Education’s National Education Technology Plan (NETP) (U.S. Department of Education 2010a). As described in the plan, increasing use of online learning offers opportunities to integrate assessment and learning so that information needed to improve future instruction can be gathered in nearly real time:

When students are learning online, there are multiple opportunities to exploit the power of technology for formative assessment. The same technology that supports learning activities gathers data in the course of learning that can be used for assessment. … An online system can collect much more and much more detailed information about how students are learning than manual methods. As students work, the system can capture their inputs and collect evidence of their problem-solving sequences, knowledge, and strategy use, as reflected by the information each student selects or inputs, the number of attempts the student makes, the number of hints and feedback given, and the time allocation across parts of the problem. (U.S. Department of Education 2010a, p. 30)

While students can clearly benefit from this detailed learning data, the NETP also describes the potential value for the broader education community through the concept of an interconnected feedback system:

decisions about learning are informed by data and that data are aggregated and made

accessible at all levels of the education system for continuous improvement.

(U.S. Department of Education 2010a, p. 35)

美国教育部教育数据挖掘概述文档

The interconnected feedback systems envisioned by the NETP rely on online learning systems collecting, aggregating, and analyzing large amounts of data and making the data available to many stakeholders. These online or adaptive learning systems will be able to exploit detailed learner activity data not only to recommend what the next learning activity for a particular

student should be, but also to predict how that student will perform with future learning content, including high-stakes examinations. Data-rich systems will be able to provide informative and actionable feedback to the learner, to the instructor, and to administrators. These learning systems also will provide software developers with feedback that is tremendously helpful in rapidly refining and improving their products. Finally, researchers will be able to use data from experimentation with adaptive learning systems to test and improve theories of teaching and learning.

In the remainder of this report, we:

1. Present scenarios that motivate research, development, and application efforts to collect and use data for personalization and adaptation.

2. Define the research base of educational data mining and learning analytics and describe the research goals researchers pursue and the questions they seek to answer about

learning at all levels of the educational system.

3. Present an abstracted adaptive learning system to show how data are obtained and used,

what major components are involved, and how various stakeholders use such systems.

4. Examine the major application areas for the tools and techniques in data mining and analytics, encompassing user and domain modeling.

5. Discuss the implementation and technical challenges and give recommendations for overcoming them.

美国教育部教育数据挖掘概述文档

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美国教育部教育数据挖掘概述文档

Online consumer experiences provide strong evidence that computer scientists are developing methods to exploit user activity data and adapt accordingly. Consider the experience a consumer has when using Netflix to choose a movie. Members can browse Netflix offerings by category (e.g., Comedy) or search by a specific actor, director, or title. On choosing a movie, the member can see a brief description of it and compare its average rating by Netflix users with that of other films in the same category. After watching a film, the member is asked to provide a simple rating of how much he or she enjoyed it. The next time the member returns to Netflix, his or her

The more a person uses Netflix, the more Netflix learns about his or her preferences and the more accurate the predicted enjoyment. But that is not all the data that are used. Because many other members are browsing, watching, and rating the same movies, the Netflix recommendation

algorithm is able to group members based on their activity data. Once members are matched, activities by some group members can be used to recommend movies to other group members. Such customization is not unique to Netfl …… 此处隐藏：5839字，全部文档内容请下载后查看。喜欢就下载吧 ……