José A. Ruipérez-Valiente

ALAS-KA

ALAS-KA is a tool that extends the learning analytics features of the Khan Academy platform.ALAS-KA includes new visualizations for the entire class and individual students of more than 20 new learning indicators.ALAS-KA helps teachers to make decision supported by the high level information provided.ALAS-KA enables students to gain awareness of their learning process for self-reflection.ALAS-KA can be used by the course instructors to detect class tendencies and learner models. The Khan Academy platform enables powerful on-line courses in which students can watch videos, solve exercises, or earn badges. This platform provides an advanced learning analytics module with useful visualizations. Nevertheless, it can be improved. In this paper, we describe ALAS-KA, which provides an extension of the learning analytics support for the Khan Academy platform. We herein present an overview of the architecture of ALAS-KA. In addition, we report the different types of visualizations and information provided by ALAS-KA, which have not been available previously in the Khan Academy platform. ALAS-KA includes new visualizations for the entire class and also for individual students. Individual visualizations can be used to check on the learning styles of students based on all the indicators available. ALAS-KA visualizations help teachers and students to make decisions in the learning process. The paper presents some guidelines and examples to help teachers make these decisions based on data from undergraduate courses, where ALAS-KA was installed. These courses (physics, chemistry, and mathematics) for freshmen were developed at Universidad Carlos III de Madrid (UC3M) and were taken by more than 300 students.

An architecture for extending the learning analytics support in the Khan Academy framework

The Khan Academy platform enables powerful on-line courses in which students can watch videos, solve exercises or earn badges. This platform provides an advanced learning analytics module with useful visualizations for teachers and students. Nevertheless, this learning analytics support can be improved with recommendations and new useful higher level visualizations in order to try to improve the learning process. In this paper, we describe our architecture for processing data from the Khan Academy platform in order to show new higher level learning visualizations and recommendations. The different involved elements of the architecture are presented and the different decisions are justified. In addition, we explain some initial examples of new useful visualizations and recommendations for teachers and students as part of our extension of the learning analytics module for the Khan Academy platform. These examples use data from an undergraduate Physics course developed at Universidad Carlos III de Madrid with more than 100 students using the Khan Academy system.

Using Multiple Accounts for Harvesting Solutions in MOOCs

The study presented in this paper deals with copying answers in MOOCs. Our findings show that a significant fraction of the certificate earners in the course that we studied have used what we call harvesting accounts to find correct answers that they later submitted in their main account, the account for which they earned a certificate. In total, around 2.5% of the users who earned a certificate in the course obtained the majority of their points by using this method, and around 10% of them used it to some extent. This paper has two main goals. The first is to define the phenomenon and demonstrate its severity. The second is characterizing key factors within the course that affect it, and suggesting possible remedies that are likely to decrease the amount of cheating. The immediate implication of this study is to MOOCs. However, we believe that the results generalize beyond MOOCs, since this strategy can be used in any learning environments that do not identify all registrants.

Towards the development of a learning analytics extension in open edX

The emergence of platforms to support MOOCs (Massive Open Online Courses) strengthens the need of a powerful learning analytics support since teachers cannot be aware of so many students. However, the learning analytics support in MOOC platforms is in an early stage nowadays. The edX platform, one of the most important MOOC platforms, has few learning analytics functionalities at present. In this paper, we analyze the learning analytics support given by the edX platform, and the main initiatives to implement learning analytics in edX. We also present our initial steps to implement a learning analytics extension in edX. We review technical aspects, difficulties, solutions, the architecture and the different elements involved. Finally, we present some new visualizations in the edX platform for teachers and students to help them understand the learning process.

Flipping the classroom to improve learning with MOOCs technology

The use of Massive Open Online Courses (MOOCs) is increasing worldwide and brings a revolution in education. The application of MOOCs has technological but also pedagogical implications. MOOCs are usually driven by short video lessons, automatic correction exercises, and the technological platforms can implement gamification or learning analytics techniques. However, much more analysis is required about the success or failure of these initiatives in order to know if this new MOOCs paradigm is appropriate for different learning situations. This work aims at analyzing and reporting whether the introduction of MOOCs technology was good or not in a case study with the Khan Academy platform at our university with students in a remedial Physics course in engineering education. Results show that students improved their grades significantly when using MOOCs technology, student satisfaction was high regarding the experience and for most of the different provided features, and there were good levels of interaction with the platform (e.g., number of completed videos or proficient exercises), and also the activity distribution for the different topics and types of activities was appropriate.

A Predictive Model of Learning Gains for a Video and Exercise Intensive Learning Environment

This work approaches the prediction of learning gains in an environment with intensive use of exercises and videos, specifically using the Khan Academy platform. We propose a linear regression model which can explain 57.4% of the learning gains variability, with the use of four variables obtained from the low level data generated by the students. We found that two of these variables are related to exercises (the proficient exercises and the average number of attempts in exercises), and one is related to both videos and exercises (the total time spent in both) related to exercises, whereas only one is related to videos.

A Demonstration of ANALYSE: A Learning Analytics Tool for Open edX

Education is being powered by technology in many ways. One of the main advantages is making use of data to improve the learning process. The massive open online course (MOOC) phenomenon became viral some years ago, and with it many different platforms emerged. However most of them are proprietary solutions (i.e. Coursera, Udacity) and cannot be used by interested stakeholders. At the moment Open edX is placed as the primary open source application to support MOOCs. The community using Open edX is growing at a fast pace with many interested institutions. Nevertheless, the learning analytics support of Open edX is still in its first steps. In this paper we present an overview and demonstration of ANALYSE, an open source learning analytics tool for Open edX. ANALYSE includes currently 12 new visualizations that can be used by both instructors and students.

A Demonstration of ALAS-KA: A Learning Analytics Tool for the Khan Academy Platform

Instructors and students have problems monitoring the learning process from low level interactions in on-line courses because it is hard to make sense of raw data. In this paper we present a demonstration of the Add-on of the Learning Analytics Support in the Khan Academy platform (ALAS-KA). Our tool processes the raw data in order to transform it into useful information that can be used by the students and instructors through visualizations. ALAS-KA is an interactive tool that allows teachers and students to select the provided information divided by courses and type of information. The demonstration is illustrated with different examples based on real experiments data.

Scaling to Massiveness With ANALYSE: A Learning Analytics Tool for Open edX

The emergence of massive open online courses (MOOCs) has caused a major impact on online education. However, learning analytics support for MOOCs still needs to improve to fulfill requirements of instructors and students. In addition, MOOCs pose challenges for learning analytics tools due to the number of learners, such as scalability in terms of computing time and visualizations. In this work, we present different visualizations of our “Add-on of the learNing AnaLYtics Support for open Edx” (ANALYSE), which is a learning analytics tool that we have designed and implemented for Open edX, based on MOOC features, teacher feedback, and pedagogical foundations. In addition, we provide a technical solution that addresses scalability at two levels: first, in terms of performance scalability, where we propose an architecture for handling massive amounts of data within educational settings; and, second, regarding the representation of visualizations under massiveness conditions, as well as advice on color usage and plot types. Finally, we provide some examples on how to use these visualizations to evaluate student performance and detect problems in resources.

Early Prediction and Variable Importance of Certificate Accomplishment in a MOOC

The emergence of MOOCs (Massive Open Online Courses) makes available big amounts of data about students’ interaction with online educational platforms. This allows for the possibility of making predictions about future learning outcomes of students based on these interactions. The prediction of certificate accomplishment can enable the early detection of students at risk, in order to perform interventions before it is too late. This study applies different machine learning techniques to predict which students are going to get a certificate during different timeframes. The purpose is to be able to analyze how the quality metrics change when the models have more data available. From the four machine learning techniques applied finally we choose a boosted trees model which provides stability in the prediction over the weeks with good quality metrics. We determine the variables that are most important for the prediction and how they change during the weeks of the course.