Even highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co-redesign methods in partnership with disciplinary experts to create high-structure STEM courses that better support students and produce…

The two main trends in the development of higher education worldwide are universal access and digital transformation. These trends are bringing about an increase in class sizes and the growth of online higher education. Previous studies indicated that both the large-class setting and online delivery threaten the quality, and the exploration of…

Replication of machine learning experiments can be a useful tool to evaluate how both "modeling" and "experimental design" contribute to experimental results; however, existing replication efforts focus almost entirely on modeling alone. In this work, we conduct a three-part replication case study of a state-of-the-art LSTM…

Research Findings: This study investigated teachers' Concept development (CD) strategy use in whole-group math teaching and its associations with children's higher-order thinking processes in 25 Chinese preschool math lessons. We utilized the CD dimension within the Classroom Assessment Scoring System (CLASS) to guide our exploration. CD…

This paper surveys the state of the art on prediction in MOOCs through a systematic literature review (SLR). The main objectives are: first, to identify the characteristics of the MOOCs used for prediction, second, to describe the prediction outcomes, third, to classify the prediction features, fourth, to determine the techniques used to predict…

Success prediction in Massive Open Online Courses (MOOCs) is now tackled in numerous works, but still needs new case studies to compare the solutions proposed. We study here a specific dataset from a French MOOC provided by the OpenClassrooms company, featuring 12 courses. We exploit various features present in the literature and test several…

MOOCs and online courses have notoriously high attrition [1]. One challenge is that it can be difficult to tell if students fail to complete because of disinterest or because of course difficulty. Utilizing a Deep Knowledge Tracing framework, we account for student engagement by including course interaction covariates. With these, we find that we…

With the rapid emergence of K-12 online learning platforms, a new era of education has been opened up. It is crucial to have a dropout warning framework to preemptively identify K-12 students who are at risk of dropping out of the online courses. Prior researchers have focused on predicting dropout in Massive Open Online Courses (MOOCs), which…

MOOCs are becoming more and more involved in the pedagogical experimentation of universities whose infrastructure does not respond to the growing mass of learners. These universities aim to complete their initial training with distance learning courses. Unfortunately, the efforts made to succeed in this pedagogical model are facing a dropout rate…

Blended courses that mix in-person instruction with online platforms are increasingly common in secondary education. These platforms record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students performance in face-to-face classes. However, predictive models…

How to identify at-risk students in open online courses has received increasing attention, since the dropout rate is unexpectedly high. Most prior studies have focused on using machine learning techniques to predict student dropout based on features extracted from students' learning activity logs. However, little work has viewed the dropout…

In 2015, 35 million learners participated online in 4,200 MOOCs organized by over 500 universities. Learning designers orchestrate MOOC content to engage learners at scale and retain interest by carefully mixing videos, lectures, readings, quizzes, and discussions. Universally, far fewer people actually participate in MOOCs than originally sign up…

Characterizing the nature of students' affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different…

Retrieval practice has been shown to produce powerful learning gains in laboratory experiments but has seldom been explored in classrooms as a means of enhancing students' learning of their course-relevant material. Furthermore, research is lacking concerning the role of individual differences in learning from retrieval. The current study explored…

With large student enrollment, MOOC instructors face the unique challenge in deciding when to intervene in forum discussions with their limited bandwidth. We study this problem of "instructor intervention." Using a large sample of forum data culled from 61 courses, we design a binary classifier to predict whether an instructor should…