Extending multiple choice questions for instructor-authored mobile practice application

通过利用当今具有技术意识的学生群体，Mathtoons产品提高了学生的 通过提高他们的参与度。他们使用重复练习的游戏化来提供更多 需要练习。由于大量的学生参加微积分课程，重点主要集中在 微积分内容，但该技术有更广泛的应用。本项目的目标是使用Mathtoons的 技术来构建并行计算课程的教育数据挖掘平台。 计算机科学教师都很清楚并发编程是一个具有挑战性的学习主题。 重复使用一个并行计算模拟器，该模拟器被增强以向学生提供自动反馈， 项目旨在开发一个平台，将记录学生在解决经典并发 编程问题。对结果的分析将提供对学生不同层次的见解 性能 数据可视化技术和统计分析将允许教师提供适当的 为适当类别的学生提供额外的材料，例如，有风险的学生将获得额外的教学材料。 支持，成绩优异者将获得奖金问题，社会学习者将被介绍给同学，等等。 利用Mathtoons的专业知识，该平台将在学生遇到困难时提供提示。它将 还可以通过提出改进建议来简化学生的解决方案， 给出一个正确或错误的答案。最后，平台会实现自动抄袭检测 该机制将用于教育学生关于剽窃和知识产权的知识。此外，它还将 允许自动检测学生协作，这将用于进一步促进社会学习者。 平台数据科学方法将为证明教学方法的合理性提供科学依据， 将形成一个有前途的研究平台。

By taking advantage of today's technology-aware student population, Mathtoons products improve students' performance by increasing their engagement. They use gamification of repetitive exercises to provide much needed practice. Due to the large number of students taking calculus courses, the focus has been mostly on calculus content but the technology has much wider applications. The present project goal is to use Mathtoons' technology to build an educational data mining platform for a parallel computing course. Computer science instructors are well aware that concurrent programming is a challenging topic to learn. Reusing a parallel computing simulator that was enhanced to provide automatic feedback to students, the project aims at developing a platform that will record student numerous attempts at solving classical concurrent programming problems. An analysis of the results will provide different levels of insights into the student performance. Data visualization techniques and statistical analysis will allow the instructor to offer the appropriate additional material to the right category of students e.g. student at risk will receive additional pedagogical support, overachievers will receive bonus questions, social learners will be introduced to fellow students, etc. Taking advantage of Mathtoons expertise, the platform will provide hints when students are struggling. It will also improve the feedback students receive by suggesting improvements to simplify their solution instead of providing a true or false answer. Finally, the platform will implement an automatic plagiarism detection mechanism that will be used to educate students on plagiarism and intellectual property. Moreover, it will also allow for the automatic detection of student collaborations, which will be used to further boost social learners. The platform data science approach will provide a scientific rationale to justify pedagogical methods, and will form a promising research platform.