Support for Doctoral Students from U.S. Universities to Attend the 11th International Conference on Educational Data Mining (EDM 2018)

支持美国高校博士生参加第十一届教育数据挖掘国际会议（EDM 2018）

• 批准号: 1840771
• 负责人: Neil Heffernan
• 金额: $ 1.96万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1840771&HistoricalAwards=false
• 关键词: Support; Doctoral; Students; U.S.; Universities

This award will support 10 doctoral students from the United States to attend the 11th Educational Data Mining (EDM) conference. Scheduled events include keynote talks by world-renowned researchers, full and short oral presentations, poster presentations, workshops, tutorials, interactive events, and the Doctoral Consortium (DC). The DC will include oral presentations and poster sessions in which students present their work. These sessions will be specially scheduled to prevent competition with other conference events and maximize attention by seasoned researchers. Students will be paired up with senior members of the EDM community who serve as mentors during the conference. The EDM conference promotes scientific research in the interdisciplinary field of educational data mining, which is concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students and the settings in which they learn. EDM is the convergence of computer science disciplinary research in databases and data mining and learning science. EDM research activities are aimed at applying data mining to analyze data generated by various information systems that support learning or education (in schools, colleges, universities, and other academic or professional learning institutions and informal learning).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将支持10名来自美国的博士生参加第11届教育数据挖掘（EDM）会议。预定的活动包括世界知名研究人员的主题演讲、完整和简短的口头报告、海报展示、研讨会、教程、互动活动和博士联盟 (DC)。 DC 将包括口头报告和海报会议，学生将在其中展示他们的作品。这些会议将经过专门安排，以防止与其他会议活动竞争，并最大限度地吸引经验丰富的研究人员的注意力。学生将与 EDM 社区的高级成员配对，他们在会议期间担任导师。 EDM 会议促进教育数据挖掘跨学科领域的科学研究，该领域致力于开发探索来自教育环境的独特数据类型的方法，并使用这些方法更好地了解学生及其学习环境。 EDM 是计算机科学数据库、数据挖掘和学习科学学科研究的融合。 EDM研究活动旨在应用数据挖掘来分析支持学习或教育（学校、学院、大学和其他学术或专业学习机构和非正式学习）的各种信息系统生成的数据。该奖项反映了NSF的法定使命，并具有通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The Effect of an Intelligent Tutor on Performance on Specific Posttest Problems

智能导师对特定后测问题表现的影响

• 发表时间: 2021-06
• 期刊: Educational Data Mining EDM 2021
• 作者: Sales, Adam;Prihar, Ethan;Heffernan, Neil;Pane, John.
• 通讯作者: Pane, John.

Identifying Explanations Within Student-Tutor Chat Logs

识别学生与导师聊天日志中的解释

• 发表时间: 2022-07
• 期刊: Educational data Mining Conference
• 作者: Prihar, Ethan;Moore, Alexander;Heffernan, Neil
• 通讯作者: Heffernan, Neil

Exploring Common Trends in Online Educational Experiments

探索在线教育实验的共同趋势

• 发表时间: 2022-07
• 期刊: Educational data Mining Conference
• 作者: Prihar, Ethan;Syed, Manaal;Ostrow, Korinn;Shaw, Stacy;Sales, Adam;Heffernan, Neil
• 通讯作者: Heffernan, Neil

Process-BERT: A Framework for Representation Learning on Educational Process Data

Process-BERT：教育过程数据表示学习框架

• DOI: 10.48550/arxiv.2204.13607
• 发表时间: 2022-04-28
• 期刊: ArXiv
• 作者: Ale;er Scarlatos;er;Christopher G. Brinton;Andrew S. Lan
• 通讯作者: Andrew S. Lan

Toward Improving Effectiveness of Crowdsourced, On-Demand Assistance From Educators in Online Learning Platforms

提高在线学习平台中教育工作者提供的众包、按需援助的有效性

• 发表时间: 2022-07
• 期刊: Educational Data Mining Conference
• 作者: haim, Aaron;Prihar, Ethan;Heffernan, Neil
• 通讯作者: Heffernan, Neil