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

Towards Student Engagement Analytics: Applying Machine Learning to Student Posts in Online Lecture Videos

The use of online learning environments in higher education is becoming ever more prevalent with the inception of MOOCs (Massive Open Online Courses) and the increase in online and flipped courses at universities. Although the online systems used to deliver course content make education more accessible, students often express frustration with the lack of assistance during online lecture videos. Instructors express concern that students are not engaging with the course material in online environments, and rely on affordances within these systems to figure out what students are doing. With many online learning environments storing log data about students usage of these systems, research into learning analytics, the measurement, collection, analysis, and reporting data about learning and their contexts, can help inform instructors about student learning in the online context. This thesis aims to lay the groundwork for learning analytics that provide instructors high-level student engagement data in online learning environments. Recent research has shown that instructors using these systems are concerned about their lack of awareness about student engagement, and educational psychology has shown that engagement is necessary for student success. Specifically, this thesis explores the feasibility of applying machine learning to categorize student posts by their level of engagement. These engagement categories are derived from the ICAP framework, which categorizes overt student behaviors into four tiers of engagement: Interactive, Constructive, Active, and Passive. Contributions include showing what natural language features are most indicative of engagement, exploring whether this machine learning method can be generalized to many courses, and using previous research to develop mockups of what analytics using data from this machine learning method might look like

Innovative learning in action (ILIA) issue five: Learning technologies in the curriculum

Consideration of the papers and snapshots in this edition of Innovative Learning in Action, focused on learning technology, will provide the reader with insights into a range of excellent and innovative approaches to the application of learning technologies to enhance learning both in the classroom and at a distance. It also provides us with examples of how learning technologies can both stimulate and support partnership with staff and students and collaborative learning and working. This edition is particularly timely given the aim of the University’s 2005-2008 Learning Technologies Implementation Plan (LTIP), which is to enhance the quality of, and access to, learning, teaching and assessment by supporting and developing the curriculum through the appropriate and effective use of learning technologies. The LTIP is designed to help us to reach a situation where the effective use of appropriate learning technologies becomes part of our normal teaching, research and enterprise activities, and enhances access to our programmes by all our students whether they are learning on campus, at a distance, or in the workplace. The emphasis at the University of Salford has consistently been on the identification and creative application of the appropriate blends of ICT and traditional methods, shaped by pedagogical, rather than technological drivers, and acknowledging and reflecting different academic contexts and professional and vocational requirements. We have some excellent examples of how this has been achieved here, ILIA once again providing us with an opportunity to reflect on practice and student learning, to share experience and hopefully to identify future areas for collaboration in a key area of curriculum development

Students’ learning experience in flipped classes using social media.

The current work aims at deploying Facebook as a social media site to improve students’ learning experience with the flipped learning materials through implementation of a socially enabled peer and selflearning environment. Provided the body of literature on poor students’ experience in flipped classes, the paper implements an experiment comparing the online quizzes and Facebook to improve students’ experience with the online materials in flipped classes. The study looks at the students’ perceptions. The paper provides recommendations to the instructors on how to use Facebook to improve students’ experience with the flipped materials. This study also motivates teaching practitioners in Information Systems to improve flipped learning by using social networking sites in their courses.N