The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempting the assessment activity. The developed model is real-time and tracks the participation of a student within a MOOC (via click-stream server logs) and predicts the performance of a student on the next as- sessment within the course offering. We perform a com- prehensive set of experiments on data obtained from three openEdX MOOCs via a Stanford University initiative. Our experimental results show the promise of the proposed ap- proach in comparison to baseline approaches and also helps in identification of key features that are associated with the study habits and learning behaviors of students.
Several researchers have focused on the analysis of education data (including MOOCs), in an effort to understand the characteristics of student learning behaviors and motivation within this education model @cite_1 . Boyer et. al. @cite_14 focus on the stopout prediction problem within MOOCs; by designing a set of processes using information from previous courses and the previous weeks of the current course. Brinton et. al. @cite_5 developed an approach to predict if a student answers a question correct on the first attempt via click-stream information and social learning networks. Kennedy et. al. @cite_19 analyzed the relationship between a student's prior knowledge on end-of-MOOC performance. Sunar et. al. @cite_18 developed an approach to predict the possible interactions between peers participating in a MOOC.
@cite_18 @cite_14 @cite_1 @cite_19 @cite_5
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High attrition rates are one of the biggest concerns in MOOCs. One of the possible causes may be learners’ lack of interactions and low levels of participations in MOOCs online discussions. Research to measure and predict recurrent interactions of learners in MOOCs online discussions has the potential to gain inside into the likely impact on the attrition rate. It is argued that personalisation in MOOCs has the potential to increase learners’ interactions and associated factors to continuous friendships. In this paper, a detailed analysis has been carried out of learners’ interactions within a MOOC. This paper investigates learners’ interaction habits and their recurrent interactions throughout the entire duration of a MOOC’s course, and consequently proposes a method to measure the interactions and predict possible interactions between peers. The findings denote that when a learner interacted with their peer, they most probably interact again in the following weeks. Moreover, our proposed prediction method also demonstrate promising results towards predicting future interactions between learners based on their previous relationships Data recorded while learners are interacting with Massive Open Online Courses (MOOC) platforms provide a unique opportunity to build predictive models that can help anticipate future behaviors and develop interventions. But since most of the useful predictive problems are defined for a real-time framework, using knowledge drawn from the past courses becomes crucial. To address this challenge, we designed a set of processes that take advantage of knowledge from both previous courses and previous weeks of the same course to make real time predictions on learners behavior. In particular, we evaluate multiple transfer learning methods. In this article, we present our results for the stopout prediction problem (predicting which learners are likely to stop engaging in the course). We believe this paper is a first step towards addressing the need of transferring knowledge across courses. This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled. While MOOCs have taken the world by storm, questions remain about their pedagogical value and high rates of attrition. In this paper we argue that MOOCs which have open entry and open curriculum structures, place pressure on learners to not only have the requisite knowledge and skills to complete the course, but also the skills to traverse the course in adaptive ways that lead to success. The empirical study presented in the paper investigated the degree to which students' prior knowledge and skills, and their engagement with the MOOC as measured through learning analytics, predict end-of-MOOC performance. The findings indicate that prior knowledge is the most significant predictor of MOOC success followed by students' ability to revise and revisit their previous work. We study student performance prediction in Massive Open Online Courses (MOOCs), where the objective is to predict whether a user will be Correct on First Attempt (CFA) in answering a question. In doing so, we develop novel techniques that leverage behavioral data collected by MOOC platforms. Using video-watching clickstream data from one of our MOOCs, we first extract summary quantities (e.g., fraction played, number of pauses) for each user-video pair, and show how certain intervals sets of values for these behaviors quantify that a pair is more likely to be CFA or not for the corresponding question. Motivated by these findings, our methods are designed to determine suitable intervals from training data and to use the corresponding success estimates as learning features in prediction algorithms. Tested against a large set of empirical data, we find that our schemes outperform standard algorithms (i.e., without behavioral data) for all datasets and metrics tested. Moreover, the improvement is particularly pronounced when considering the first few course weeks, demonstrating the “early detection” capability of such clickstream data. We also discuss how CFA prediction can be used to depict graphs of the Social Learning Network (SLN) of students, which can help instructors manage courses more effectively.