Mining a MOOC to examine international views of the “Smart City”

Increasing numbers of cities are focussed on using technology to become “Smart”. Many of these Smart City programmes are starting to go beyond a technological focus to also explore the value of a more inclusive approach that values the input of citizens. However, the insights gained from working with citizens are typically focused around a single town or city. In this paper we explore whether it is possible to understand people’s opinions and views on the Smart City topics of Open Data, privacy and leadership by examining comments left on a Smart City MOOC that has been delivered internationally. In doing so we start to explore whether MOOCs can provide a lens for examining views on different facets of the Smart City agenda from a global audience, albeit limited to the demographic of the typical MOOC user

IDENTIFICATION OF STUDENTS AT RISK OF LOW PERFORMANCE BY COMBINING RULE-BASED MODELS, ENHANCED MACHINE LEARNING, AND KNOWLEDGE GRAPH TECHNIQUES

Technologies and online learning platforms have changed the contemporary educational paradigm, giving institutions more alternatives in a complex and competitive environment. Online learning platforms, learning-based analytics, and data mining tools are increasingly complementing and replacing traditional education techniques. However, academic underachievement, graduation delays, and student dropouts remain common problems in educational institutions. One potential method of preventing these issues is by predicting student performance through the use of institution data and advanced technologies. However, to date, scholars have yet to develop a module that can accurately predict students’ academic achievement and commitment. This dissertation attempts to bridge that gap by presenting a framework that allows instructors to achieve four goals: (1) track and monitor the performance of each student on their course, (2) identify at-risk students during the earliest stages of the course progression (3), enhance the accuracy with which at-risk student performance is predicted, and (4) improve the accuracy of student ranking and development of personalized learning interventions. These goals are achieved via four objectives. Objective One proposes a rule-based strategy and risk factor flag to warn instructors about at-risk students. Objective Two classifies at-risk students using an explainable ML-based model and rule-based approach. It also offers remedial strategies for at-risk students at each checkpoint to address their weaknesses. Objective Three uses ML-based models, GCNs, and knowledge graphs to enhance the prediction results. Objective Four predicts students’ ranking using ML-based models and clustering-based KGEs with the aim of developing personalized learning interventions. It is anticipated that the solution presented in this dissertation will help educational institutions identify and analyze at-risk students on a course-by-course basis and, thereby, minimize course failure rates

Bridging the Geospatial Education-Workforce Divide: A Case Study on How Higher Education Can Address the Emerging Geospatial Drivers and Trends of the Intelligent Web Mapping Era

The purpose of this exploratory collective case study is to discover how geospatial education can meet the geospatial workforce needs of the Commonwealth of Virginia, in the emerging intelligent web mapping era. Geospatial education uses geographic information systems (GIS) to enable student learning by increasing in-depth spatial analysis and meaning using geotechnology tools (Baker & White, 2003). Bandura’s (1977) self-efficacy theory and geography concept of spatial thinking form an integrated theoretical framework of spatial cognition for this study. Data collection included in-depth interviews of twelve geospatial stakeholders, documentation collection, and supporting Q methodology to determine the viewpoints of a total of 41 geospatial stakeholders. Q methodology is a type of data collection that when used as a qualitative method utilizes sorting by the participant to determine their preferences. Data analysis strategies included cross-case synthesis, direct interpretation, generalizations, and a correlation matrix to show similarities in participants\u27 preferences. The results revealed four collaborative perceptions of the stakeholders, forming four themes of social education, technology early adoption, data collaboration, and urban fundamentals. Four strategies were identified for higher education to prepare students for the emerging geospatial workforce trends. These strategies are to teach fundamentals, develop agile faculty and curriculum, use an interdisciplinary approach, and collaborate. These strategies reflect the perceptions of stakeholders in this study on how higher education can meet the emerging drivers and trends of the geospatial workforce

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

Analysing students’ cognitive engagement in e-learning discussion forums through content analysis

Discussion forums provide students with accessible platforms for group discussions in e-learning environments. They also help lecturers to track and check student discussions. To improve student learning, it is important for lecturers to identify students’ cognitive engagement in discussion forums. Therefore, this study aims to investigate students’ cognitive engagement in e-learning through content analysis of forum posts. A total of 267 forum posts created by students during one semester were collected for analysis. Inferential statistics were applied to explore the relationship between students’ cognitive engagement and their gender and the number of posts in forums. The results revealed that about half of the students gave their posts without any explanations, which reflected a low level of cognitive engagement. A large number of posts made little contribution to a high level of cognitive engagement. The results also showed no relationship between gender and the level of cognitive engagement. The limitations and implications of this study are also discussed

Language learning and technology

By and large, languages, both as first, second or foreign languages remain one of the most important core subjects at every educational level. In early stages, their inclusion in the curriculum is intricately connected with (pre-)literacy practices, but also as a main driver for the successful integration of minority students learning a second language. In addition, the attainment of a certain level of a foreign language by the end of compulsory education is a common goal in most educational systems around the globe. Arguably, the key drivers of success in learning a language range from motivational to attitudinal, but ultimately they also have to do with the amount of target language use, the access to quality input, and especially language teachers' readiness to incorporate the latest educational trends effectively in the language classroom, educational technologies amongst them

Sentiment Analysis of Nigerian Students’ Tweets on Education: A Data Mining Approach

The paper is aimed at investigating data mining technologies by acquiring tweets from Nigerian University students on Twitter on how they feel about the current state of the Nigerian university system. The study for this paper was conducted in a way that the tweet data collected using the Twitter Application was pre-processed before being translated from text to vector representation using a feature extraction technique such Bag-of-Words. In the paper, the proposed sentiment analysis architecture was designed using UML and the Naïve Bayes classifier (NBC) approach, which is a simple but effective classifier to determine the polarity of the education dataset, was applied to compute the probabilities of the classes. Furthermore, Naïve Bayes classifier polarized the tweets' wording as negative or positive for polarity. Based on our investigation, the experiment revealed after data cleaning that 4016 of the total data obtained were utilized. Also, Positive attitudes accounted for 40.56%, while negative sentiments accounted for 59.44% of the total data having divided the dataset into 70:30 training and testing ratio, with the Naïve Bayes classifier being taught on the training set and its performance being evaluated on the test set. Because the models were trained on unbalanced data, we employed more relevant evaluation metrics such as precision, recall, F1-score, and balanced accuracy for model evaluation. The classifier's prediction accuracy, misclassification error rate, recall, precision, and f1-score were 63 %, 37%, 63%, 62%, and 62% respectively. All of the analyses were completed using the Python programming language and the Natural Language Tool Kit packages. Finally, the outcome of this prediction is the highest likelihood class. These forecasts can be used by Nigerian Government to improve the educational system and assist students to receive a better education

Engagement of Learners Undertaking Massive Open Online Courses and the Impact of Design

This thesis investigates the low levels of student engagement after registering to study for a massive open online course. To do this, it adopts a mixed methods approach (Gray, 2013) by analysing two large-scale surveys (120,842 and 1,800 responses respectively) and interviewing 12 learners. This was possible because access was given to 76 presentations of 19 MOOCs produced by The Open University on the FutureLearn platform. The aim of this thesis was to answer two research questions. Why do learners engage in massive open online courses (MOOCs), and what elements of the design of MOOCS encourage learner engagement? The analysis of 120,842 survey responses illustrated that learners across all the MOOCs investigated in this study were very focussed on personal interest, regardless of subject. Courses with subject material which focussed upon the future use of technology and educational technology were embarked upon for professional purposes secondary to personal interest. Learners interviewed who had not completed the MOOCs did not see themselves as disengaged but as having achieved their study goals. Learning designs of 19 MOOCs with learner activity and dashboard data from 800,038 enrolments and 425,792 learners were analysed with respect to the second research question. The activity data from 425,792 learners demonstrated they were more likely to engage with comments and to like comments on steps such as articles and videos than on discussion steps. Findings from the performance dashboard data (for example enrolment numbers) and learner activity data, coupled with learning designs, were analysed. From this, high-engagement steps (‘Super Steps’) were identified and isolated for analysis. This study discovered that learners preferred to engage with steps that the learning design framework classified as communicative or assimilative. Learners were more likely to engage with steps that posed questions within their titles, a previously unconsidered element within learning design