Lukasz Kidzinski

MOOC Video Interaction Patterns: What Do They Tell Us?

For MOOC learners, lecture video viewing is the central learning activity. This paper reports a large-scale analysis of in-video interactions. We categorize the video behaviors into patterns by employing a clustering methodology, based on the available types of interactions, namely, pausing, forward and backward seeking and speed changing. We focus on how learners view MOOC videos with these interaction patterns, especially on exploring the relationship between video interaction and perceived video difficulty, video revisiting behaviors and student performance. Our findings provide insights for improving the MOOC learning experiences.

Semiautomatic Annotation of MOOC Forum Posts

Massive online open courses’ (MOOCs’) students who use discussion forums have higher chances of finishing the course. However, little research has been conducted for understanding the underlying factors. One of the reasons which hinders the analysis is the amount of manual work required for annotating posts. In this paper, we use machine learning techniques to extrapolate small set of annotations to the whole forum. These annotations not only allow MOOC producers to summarize the state of the forum, but they also allow researchers to deeper understand the role of the forum in the learning process.

How to Quantify Student's Regularity?

Studies carried out in classroom-based learning context, have consistently shown a positive relation between students’ conscientiousness and their academic success. We hypothesize that time management and regularity are main constructing blocks of students’ conscientiousness in the context of online education. In online education, despite intuitive arguments supporting on-demand courses as more flexible delivery of knowledge, completion rate is higher in the courses with rigid temporal constraints and structure. In this study, we further investigate how students’ regularity affects their learning outcome in MOOCs. We propose several measures to quantify students regularity. We validate accuracy of these measures as predictors of students’ performance in the course.

A tutorial on machine learning in educational science

Popularity of massive online open courses (MOOCs) allowed educational researchers to address problems which were not accessible few years ago. Although classical statistical techniques still apply, large datasets allow us to discover deeper patterns and to provide more accurate predictions of student’s behaviors and outcomes. The goal of this tutorial was to disseminate knowledge on elementary data analysis tools as well as facilitate simple practical data analysis activities with the purpose of stimulating reflection on the great potential of large datasets. In particular, during the tutorial we introduce elementary tools for using machine learning models in education. Although the methodology presented here applies in any programming environment, we choose R and CARET package due to simplicity and access to the most recent machine learning methods.

Orchestration Load Indicators and Patterns: In-the-Wild Studies Using Mobile Eye-Tracking

Orchestration load is the effort a teacher spends in coordinating multiple activities and learning processes. It has been proposed as a construct to evaluate the usability of learning technologies at the classroom level, in the same way that cognitive load is used as a measure of usability at the individual level. However, so far this notion has remained abstract. In order to ground orchestration load in empirical evidence and study it in a more systematic and detailed manner, we propose a method to quantify it, based on physiological data (concretely, mobile eye-tracking measures), along with human-coded behavioral data. This paper presents the results of applying this method to four exploratory case studies, where four teachers orchestrated technology-enhanced face-to-face lessons with primary, secondary school, and university students. The data from these studies provide a first validation of this method in different conditions, and illustrate how it can be used to understand the effect of different classroom factors on orchestration load. From these studies, we also extract empirical insights about classroom orchestration using technology.

Augmenting Collaborative MOOC Video Viewing with Synchronized Textbook

We designed BOOC, an application that synchronizes textbook content with MOOC (Massive Open Online Courses) videos. The application leverages a tablet display split into two views to present lecture videos and textbook content simultaneously. The display of the book serves as peripheral contextual help for video viewing activities. A five-week user study with 6 groups of MOOC students in a blended on-campus course was conducted. Our study in this paper reports how textbooks are used in authentic MOOC study groups and further explores the effects of the book-mapping feature of the BOOC player in enhancing the collaborative MOOC learning experiences.