Discussion Analytics: Identifying Conversations and Social Learners in FutureLearn MOOCs

Discussion among learners in MOOCs has been hailed as beneficial for social constructive learning. To understand the pedagogical value of MOOC discussion forums, several researchers have utilized content analysis techniques to associate individual postings with differing levels of cognitive activity. However, this analysis typically ignores the turn taking among discussion postings, such as learners responding to others’ replies to their posts, learners receiving no reply for their posts, or learners just posting without conversing with others. This information is particularly important in understanding patterns of conversations that occur in MOOCs, and learners’ commenting behaviors. Therefore, in this paper we categorize comments in a FutureLearn MOOC based on their nature (post vs. reply to others’ post), classify learners based on their contributions for each type of post-ing, and identify conversations based on the types of comments composing them. This categorization quantifies the dynamics of conversations in the discussion activities, allowing monitoring of on-going discussion activities in FutureLearn and further analysis of identified conversations, social learners, and course steps with an unusually high number of a particular type of comment

Crowdsourcing Cognitive Presence: A Quantitative Content Analysis of a K12 Educator MOOC Discussion Forum

Massively Open Online Courses (MOOCs) offer participants opportunities to engage with content and discussion forums similar to other online courses. Pedagogical components of MOOCs and the nature of learning are worth of examining due to issues involving scale, interaction and the role of the instructor (Ross, Sinclair, Know, Bayne & McLeod, 2014). The Community of Inquiry (CoI) framework provides a basis for measuring cognitive presence in online discussion forums. As voluntary point of entry to a community of learners, it is important to consider the nature of participant contributions in terms of cognitive presence. This study focused on an educator MOOC because MOOCs have been proposed as an efficient vehicle for providing professional development due to the significant self-identification of participants as educators (Ho et al. 2014). Participant attributes have been categorized, however the discussion forum is difficult to study on a massive scale (Kizilcec, Piech, & Schulz, 2013). Automated measures of cognitive presence may not provide the full view of learning behaviors implicit in messages posted to the forums (Wong, Pursel, Divinsky & Jansen, 2015). To address this gap, the forum messages were hand-coded and analyzed using quantitative content analysis (Neuendorf, 2002). The study found that the measure of exploration increased over the duration of the course. Viewing cognitive presence over time provided a new metaphor for explaining the proportions of cognitive presence in the discussion forum of an educator MOOC. This finding suggests that increased instructor presence during the later stages of the course may increase cognitive presence over time (Akyol & Garrison, 2007; Garrison & Cleveland-Innes, 2005)

Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

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

Automatic Identification of Questions in MOOC Forums and Association with Self-Regulated Learning

International audienceDiscussion forums can be a rich source to analyze students' questions but it can be challenging to find relevant categories of questions. We considered here students' posts from the discussion forum of four editions of a same French MOOC on Project Management. We extended a coding scheme to annotate questions based on their content (course vs. non course) and trained 3 stages of an automatic annotation model. Then we studied the correlation between the nature of the questions asked and students' performance and self-regulation. The results are promising and reveal, for the minority of students active on forums, the possibility to use this feature to better estimate their performance and some of their self-regulation skills based on questions they ask

Why did Nobody Reply to Me? A Keyword Analysis of Initiating Posts and Lone Posts in Massive Open Online Courses (MOOCs) Discussions

It is a common phenomenon that online discussion spaces are overabundant with lone posts; in other words, few posts receive replies from others. Admittedly, circumstantial factors and content affect whether a post receives replies. Yet, linguistic features within a post might also play a role in inviting replies. To investigate this hypothesis, a keyword analysis comparing initiating posts, which receive replies, to lone posts, which do not receive replies, was conducted. The posts were from the discussion in massive open online courses (MOOCs). MOOC discussion is one type of computer-mediated communication (CMC), with an emphasis on learning and is typically monitored by course facilitators. The keyword analysis revealed that initiating posts were often constructed in a question format, with hedges and indefinite pronouns to open up a dialogue and invite others to pitch in, whereas lone posts tended to be reflective and monoglossic in nature, yet with positive sentiments

MOOC (Massive Open Online Courses)

Massive Open Online Courses (MOOCs) are free online courses available to anyone who can sign up. MOOCs provide an affordable and flexible way to learn new skills, advance in careers, and provide quality educational experiences to a certain extent. Millions of people around the world use MOOCs for learning and their reasons are various, including career development, career change, college preparation, supplementary learning, lifelong learning, corporate e-Learning and training, and so on

An algorithm and a tool for the automatic grading of MOOC learners from their contributions in the discussion forum

MOOCs (massive open online courses) have a built-in forum where learners can share experiences as well as ask questions and get answers. Nevertheless, the work of the learners in the MOOC forum is usually not taken into account when calculating their grade in the course, due to the difficulty of automating the calculation of that grade in a context with a very large number of learners. In some situations, discussion forums might even be the only available evidence to grade learners. In other situations, forum interactions could serve as a complement for calculating the grade in addition to traditional summative assessment activities. This paper proposes an algorithm to automatically calculate learners' grades in the MOOC forum, considering both the quantitative dimension and the relevance in their contributions. In addition, the algorithm has been implemented within a web application, providing instructors with a visual and a numerical representation of the grade for each learner. An exploratory analysis is carried out to assess the algorithm and the tool with a MOOC on programming, obtaining a moderate positive correlation between the forum grades provided by the algorithm and the grades obtained through the summative assessment activities. Nevertheless, the complementary analysis conducted indicates that this correlation may not be enough to use the forum grades as predictors of the grades obtained through summative assessment activities.This work was supported in part by the FEDER/Ministerio de Ciencia, Innovación y Universidades;Agencia Estatal de Investigación, through the Smartlet Project under Grant TIN2017-85179-C3-1-R, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307, a project which is co-funded by the European Structural Funds (FSE and FEDER). Partial support has also been received from the European Commission through Erasmus+ Capacity Building in the Field of Higher Education projects, more specifically through projects LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), InnovaT (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), and PROF-XXI (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP)