Cyberlearning: Detecting and Predicting Procrastination in Online and Social Learning

随着在线教育越来越容易获得雇主和学生的信任，许多工人正在转向在线课程，以提高他们的教育和就业前景。 然而，在线课程需要有效的时间管理技能，因为学生需要计划和设定目标，管理他们的时间，并自己（或小组）工作，通常比面对面课程的结构少。 这增加了拖延的风险，这是时间管理和工作和教育环境中成功的关键挑战。 为了解决这些风险，该项目将使用计算算法来模拟学生的拖延行为，识别未来可能出现的拖延的指标，并在个人和小组工作中及早发现。 该算法将根据时间管理应用程序捕获的学习者的学习行为及其在课程中的表现来学习预测拖延。该项目的研究结果可用于提高学生的学习，帮助他们设定目标和计划他们的工作，监测他们的进度，并跟踪他们需要做什么，以按时成功完成作业。 这些研究结果可以应用于劳动力开发等相关领域，开发的数据收集工具和算法也将提供给其他希望研究行为和学习交叉点相关问题的研究人员。本项目利用学生自我报告的认知、元认知、动机和情感过程的数据，开发计算模型，对个人和群体的拖延行为进行研究。本研究将基于学生对学业相关拖延的自我评价的横截面自我报告调查数据，以及学生在课程期间使用的移动的应用程序产生的学习和互动行为的时间戳跟踪数据，对当前的拖延理论进行研究和扩展。本研究的网络学习进步是：（1）一个新的个人和个人在群体（社会）中的拖延模型，基于自我报告和跟踪数据来检测拖延;（2）一个新的模型，基于他们的拖延，以前的任务完成行为和以前的表现来预测学生的表现;（3）探索自我报告和追踪数据的最简约组合，以产生有效的拖延模型。这些目标将通过以下方式实现：（a）开发和更新用于数据收集和调查管理的应用程序，（B）在几个研究生在线课程中部署该应用程序，（c）分析数据以了解潜在的拖延过程，以及（d）开发机器学习算法来建模和检测拖延。这个奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

As online education becomes increasingly available and trusted by both employers and students, many workers are turning to online courses to advance their education and job prospects. However, online courses demand effective time management skills, as students are required to plan and set goals, manage their time, and work by themselves (or in a group), often with less structure than an in-person course. This increases the risks of procrastination, a key challenge to time management and success in both work and education contexts. To address those risks, this project will use computational algorithms to model students' procrastination behaviors, identify indicators of likely future procrastination, and detect it early on in both individual and in group work. The algorithms will learn to predict procrastination according to learners' studying behavior captured by a time management application and their performance in courses. The findings of this project can be used to enhance students' learning by helping them to set goals and plan their work, monitor their progress, and keep track of what they need to do to successfully accomplish their assignments on time. These findings can be applied to related areas such as workforce development, and the data collection tools and algorithms developed will be made available to other researchers who want to work on related questions at the intersection of behavior and learning.This project examines individual and group procrastination behavior by developing computational models using data on students' self-reported cognitive, metacognitive, motivational, and affective processes. Current theories of procrastination will be studied and extended based on cross-sectional self-report survey data asking for student self-ratings of procrastination related to academic tasks, and time-stamped trace data of studying and interaction behavior generated by a mobile app used by students during their courses. The cyberlearning advancements of this study are (1) a novel model of individual and individual-in-group (social) procrastination, to detect procrastination based on both self-report and trace data; (2) a novel model to predict student performance based on their procrastination, previous task accomplishment behavior, and previous performance; and (3) exploration of the most parsimonious combination of self-report and trace data to produce effective procrastination model. These goals will be accomplished by (a) developing and updating an application for data collection and survey administration, (b) deploying the app in several graduate online courses, (c) analyzing data to understand underlying procrastination processes, and (d) developing machine learning algorithms to model and detect procrastination. The project will result in the dissemination of findings and developed algorithms to the broader field of sequential data science.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.