Predicting Paid Certification in Massive Open Online Courses

Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

NGATI MUKUWERENGA NDICHEDWA KALE KALE PIECES OF THE PUZZLE: AID, EDUCATION AND GEOGRAPHY IN MALAWI A COMPARISON OF GIS AND MANUAL METHODS FOR INCREASING OWNERSHIP OPPORTUNITIES IN INTERNATIONAL AID TO EDUCATION IN MALAWI

School construction remains a crucial tool for international aid organizations (IAOs) that seek to eliminate barriers to economic growth and educational attainment in Malawi and throughout Africa (Moss, 2011; Sperling & Winthrop, 2015). The process of selecting sites for new school construction is a difficult task for IAOs because of their need to select sites using methods that are as “impartial,” “equitable,” and “data-driven” as possible (Mawdsley, 2017). As such, the selection process can be lengthy, siloed, and feature limited involvement from the Government of Malawi (Collins, 2011). Ultimately, the selection process for new school construction can undermine IAOs efforts at increasing ownership of foreign aid projects by the Government of Malawi (Chirwa, 2012; Bizhan, 2016). This research study uses a mixed-methods case study design to explore how Geographic Information Systems (GIS) can be utilized in the process of selecting sites for construction. Using my experience living and working in Malawi and Mixed Methods GIS, the study compares manual and system generated sites for future construction. Criteria for manual and GIS sites include quantitative data provided by the Malawian Ministry of Education, Science, and Technology (MOEST) analyzed with linear regressions and multilevel modeling. GIS processing was completed using ArcGIS Pro. The GIS-generated results were compared with manually-generated site selections, revealing that the GIS process featured more opportunities for partnership in future site selections. Keywords:Doctor of Philosoph

Multi-level computer aided learner assessment in massive open online courses

International audienceAssessment is at the heart of massive open online courses (MOOC) challenges. It is also a core component for any effective learning. In this paper, we provide a general survey of the various forms of assessment in MOOCs. Then, we propose gradual automated learners assessment based on ontology driven for auto-evaluation learning approach (ODALA) approach. Our proposition focuses on an assessment pyramid with four levels: Closed-ended questions, Half-open questions, Open-ended questions and problem solving (PS). This pyramid is the backbone of the learning process since it needs a gradual progression with an adequate methodology. Various computer aided or completely automated assessment activities are proposed. The transition from a level to another is a conditional one since there are minimal threshold of disciplinary knowledge acquisition. An evaluation prototype was tested with the Algorithmic discipline and was developed to access the feasibility of our proposition