Natan Sarit, Lazebnik Teddy, Lerner Elisa
Department of Mathematics, Bar-Ila University, Ramat Gan, Israel.
Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel.
SN Soc Sci. 2022;2(4):46. doi: 10.1007/s43545-022-00337-4. Epub 2022 Apr 15.
Online (web-based) courses emerged at the beginning of the twenty-first century. While this new pedagogic paradigm (PP) holds a promise of better learning and training, it comes with challenges as traditional PPs are not suited to the new settings which highly differ from the physical classroom methods. We studied three online PPs (Synchronous, Asynchronous, and Asynchronous with an audience) and their influence on the students learning process and achievements during an academic mathematics course that was conducted online. 168 students took an exam and answered a questionnaire regarding their learning preferences, experience, and habits after experiencing one of these PPs. We found that students who studied according to the asynchronous with an audience PP achieved a higher score in the exam, regardless of their initial level than students who learned by either the synchronous or asynchronous PPs. In addition, we developed a personalized model based on machine learning methods that match an online PP for each student to maximize the student's score in the exam. In the case of an academic mathematical course, the online PP had a major influence on the students' scores in the exam. We found that students with high grades in previous courses preferred synchronous learning, which indicates the importance of picking the right online PP for each student. Our model provides a novel tool for the pedagogic community to personalize online learning by recommending the PP that could be most suitable for each student.
在线(基于网络的)课程出现在21世纪初。虽然这种新的教学范式(PP)有望带来更好的学习和培训,但它也带来了挑战,因为传统的教学范式并不适用于与实体课堂方法有很大差异的新环境。我们研究了三种在线教学范式(同步、异步以及有观众参与的异步)及其在一门在线学术数学课程中对学生学习过程和成绩的影响。168名学生在体验了其中一种教学范式后参加了考试,并回答了一份关于他们学习偏好、经验和习惯的问卷。我们发现,无论初始水平如何,按照有观众参与的异步教学范式学习的学生在考试中取得的分数都高于通过同步或异步教学范式学习的学生。此外,我们基于机器学习方法开发了一个个性化模型,为每个学生匹配一种在线教学范式,以最大化学生在考试中的分数。在学术数学课程的情况下,在线教学范式对学生的考试成绩有重大影响。我们发现,之前课程成绩高的学生更喜欢同步学习,这表明为每个学生选择合适的在线教学范式很重要。我们的模型为教学界提供了一种新颖的工具,通过推荐最适合每个学生的教学范式来实现在线学习的个性化。