Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions

In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into cognitively plausible higher level behaviors, and construct a quantitative information processing index, which can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illustrate how such a metric inspired by cognitive psychology can help answer critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video & course dropouts. Implications for research and practice are discusse

Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners

This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video clickstream interactions and forum activities, we seek to fundamentally understand traits that are predictive of decreasing engagement over time. Grounded in the interdisciplinary field of network science, we follow a graph based approach to successfully extract indicators of active and passive MOOC participation that reflect persistence and regularity in the overall interaction footprint. Using these rich educational semantics, we focus on the problem of predicting student attrition, one of the major highlights of MOOC literature in the recent years. Our results indicate an improvement over a baseline ngram based approach in capturing "attrition intensifying" features from the learning activities that MOOC learners engage in. Implications for some compelling future research are discussed.Comment: "Shared Task" submission for EMNLP 2014 Workshop on Modeling Large Scale Social Interaction in Massively Open Online Course

An analysis of learner arguments in a collective learning environment

This contribution analyses the arguments of students in a learning activity entitled "Argue Graph". This activity is intended to make students understand the relationship between learning theories and design choices in courseware development. The analysis of arguments is centered on the effects of discussion and opinion conflict on the elaboration of arguments. We then use an adaptation of a collective intelligence model to describe the knowledge flow among people and artifacts during the learning activity. Finally, the representations produced by the system, used by students to write a synthesis and by the teacher to debrief the class are analysed in relation to metacognition. We propose to consider these representations as metacognitive tools which structure the learning activity

From mirroring to guiding: A review of the state of art technology for supporting collaborative learning

We review systems that support the management of collaborative interaction, and propose a classification framework built on a simple model of coaching. Our framework distinguishes between mirroring systems, which display basic actions to collaborators, metacognitive tools, which represent the state of interaction via a set of key indicators, and coaching systems, which offer advice based on an interpretation of those indicators. The reviewed systems are further characterized by the type of interaction data they assimilate, the processes they use for deriving higher-level data representations, and the type of feedback they provide to users

Digital Ethics Canvas: A Guide For Ethical Risk Assessment And Mitigation In The Digital Domain

Ethical concerns in the digital domain are growing with the extremely fast evolution of technology and the increasing scale at which software is deployed, potentially affecting our societies globally. It is crucial that engineers evaluate more systematically the impacts their solutions can have on individuals, groups, societies and the environment. Ethical risk analysis is one of the approaches that can help reduce “ethical debt”, the unpaid cost generated by ethically problematic technical solutions. However, previous research has identified that novices struggle with the identification of risks and their mitigation. Our contribution is a visual tool, the Digital Ethics Canvas, specifically designed to help engineers scan digital solutions for a range of ethical risks with six “lenses”: beneficence, non-maleficence, privacy, fairness, sustainability and empowerment. In this paper, we present the literature background behind the design of this tool. We also report on preliminary evaluations of the canvas with novices (N=26) and experts (N=16) showing that the tool is perceived as practical and useful, with positive utility judgements from participants

Unravelling cross-recurrence: coupling across timescales

We present an extension of cross-recurrence analysis for dual gaze analysis which is suited for complex situations where for instance the objects of interest are not all visible at all times or when stimulus exploration is not homogeneous. The typicical situation is a visual stimulus that is scrolled or that is explored sequentially. We use a recurrence simulation to illustrate how to measure the actual coupling between behavior streams without biases introduced by the complexity of the situation. Our method takes into account underlying random baselines to compute an unbiased version of the coupling

How important is the credit channel? An empirical study of the US banking crisis

We examine whether by adding a credit channel to the standard New Keynesian model we can account better for the behaviour of US macroeconomic data up to and including the banking crisis. We use the method of indirect inference which evaluates statistically how far a model's simulated behaviour mimics the behaviour of the data. We find that the model with credit dominates the standard model by a substantial margin. Credit shocks are the main contributor to the variation in the output gap during the crisis

“With-me-ness”: A gaze-measure for students’ attention in MOOCs

We propose a gaze-based indicator of students’ attention in a MOOC video lecture. We report the results from an eye-tracking study during a MOOC lecture. We define the gaze- based indicator of students’ attention as “with-me-ness”. This answers a question from teachers’ perspective “how much are the students with me?” With-me-ness is defined at two levels: perceptual, following teacher’s deictic acts- and conceptual – following teacher discourse. We conducted an experiment with 40 participants and observed a significant and positive correlation between the two levels of with-me-ness and the posttest scores

Displaying Teacher's Gaze in a MOOC: Effects on Students' Video Navigation Patterns

We present an eye-tracking study where we augment a Massive Open Online Course (MOOC) video with the gaze information of the teacher. We tracked the gaze of a teacher while he was recording the content for a MOOC lecture. Our working hypothesis is that displaying the gaze of the teacher will act as cues in crucial moments of dyadic conversation, the teacher-student dyad, such as reference disambiguation. We collected data about students' video interaction behaviour within a MOOC. The results show that the showing the teacher's gaze made the content easier to follow for the students even when complex visual stimulus present in the video lecture