Predictive Modelling of Student Behavior Using Granular Large-Scale Action Data

Abstract

Digital learning environments generate a precise record of the actions learners take as they interact with learning materials and complete exercises towards comprehension. With this high quantity of sequential data comes the potential to apply time series models to learn about underlying behavioral patterns and trends that characterize successful learning based on the granular record of student actions. There exist several methods for looking at longitudinal, sequential data like those recorded from learning environments. In the field of language modelling, traditional n-gram techniques and modern recurrent neural network (RNN) approaches have been applied to algorithmically find structure in language and predict the next word given the previous words in the sentence or paragraph as input. In this paper, we draw an analogy to this work by treating student sequences of resource views and interactions in a MOOC as the inputs and predicting students' next interaction as outputs. In this study, we train only on students who received a certificate of completion. In doing so, the model could potentially be used for recommendation of sequences eventually leading to success, as opposed to perpetuating unproductive behavior. Given that the MOOC used in our study had over 3,500 unique resources, predicting the exact resource that a student will interact with next might appear to be a difficult classification problem. We find that simply following the syllabus (built-in structure of the course) gives on average 23% accuracy in making this prediction, followed by the n-gram method with 70.4%, and RNN based methods with 72.2%. This research lays the ground work for recommendation in a MOOC and other digital learning environments where high volumes of sequential data exist.

Communication at Scale in a MOOC Using Predictive Engagement Analytics

“…Within a set time period of one week, various representative values were calculated to summarize a learner's behavior. We constructed twelve features, most of which were taken from a thoroughly-described set of features of a similar experiment by [13], but with week-by-week comparison features removedwhile [13] predicted when a learner would drop-out, we are focusing on if the learner eventually receives certification. To account for the loss of information about how far a learner has progressed through the course, we included two extra features not included in [13] (see features 6 and 12 in Table 2).…”

“…Very few studies have combined predictive modeling with real-world interventions in a MOOC. In [20], next resource suggestions were made using a predictive model of behavior [19]. On residential campuses, predictive models of drop-out have been operationalized in the form of dispatching counselors for flagged students [18], an approach which can have unintended side effects of signaling to students that they are not likely to pass the course, and thus catalyzing a greater rate of drop-out than without the intervention.…”

Analysis of Student Behaviour in Habitable Worlds Using Continuous Representation Visualization

“…The novel intuition of our application of this to student course sequences is that instead of learning the structure of language by training on sequences of words, we are learning the structure of learner behaviour from sequences of page views. It was previously found that clickstream behaviours within MOOCs could be predicted using a Recurrent Neural Network (RNN) with 70% accuracy, compared to the 45% accuracy provided by the expected path through the course when following the existing course structure (Tang, Peterson, & Pardos, 2017). This work builds on the observation that patterns exist in learner clickstream behaviours.…”

Big data in education and the models that love them

“…In an online course context, the available input includes event stream (or clickstream) data which features inputs of mixed type. The output can be any logged outcome, such as certification in the course [23], stop-out [24,25] or predicting what action the learner is going to take next in the course [26 ]. In this case, behavior is the input and the output.…”