The landscape of MOOCs and Higher Education in Europe and the USA

Oral presentation at the European MOOCs Stakeholders Summit (EMOOCs 2019). Naples (IT), 20-22 May 201

Using Micro-Credentials to Promote Effective Teacher Professional Development: A Case Study from Xi’an Jiaotong-Liverpool University

This study illuminates the characteristics of micro-credentials, which can effectively meet the needs of working professionals in higher education for teacher professional development and career competence-building

MOOCs und Microcredentials: Internationale und österreichische Entwicklungen

Der Beitrag zeichnet die Entwicklung der letzten Jahre von MOOCs in Hinblick auf die Entwicklungen im Bereich Microcredentials im Hochschulraum nach. Dabei zeigt sich, wie international die Debatte um „kleine“ Zertifikate der Hochschulen von MOOC-Plattformen dominiert wird, aber es noch nicht viele Beispiele für die Anrechnung der Bescheinigungen an Hochschulen gibt. Die europäische sowie auch österreichische Debatte beschäftigt sich mit Maßnahmen zur Vergleichbarkeit und Nutzung von Standards, MOOCs werden dabei kaum erwähnt – aber mit dem Rahmenwerk des European MOOC Consortium wird ein interessanter Beitrag zu Debatte geleistet. Schließlich wird die Einführung der ersten österreichischen Microcredentials unter Verwendung von MOOCs durch die TU Graz und deren Nutzung beschriebe

How might micro-credentials influence institutions and empower learners in higher education?

Micro-credentials are increasingly considered a key mechanism through which to empower learners by enabling flexible upskilling and reskilling. Despite their apparent importance for higher education institutions (HEIs) and learners, empirical research is limited. More needs to be understood, particularly about the ways in which micro-credentials can shape institutional practice and provide benefits to learners.publishedVersionPeer reviewe

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

International Open and Distance Learning Conference proceedings book = Uluslararası Açık ve Uzaktan Öğrenme Konferansı bildiri kitabı

The 4th International Open & Distance Learning Conference- IODL 2019, which was held at Anadolu University, Eskişehir, Türkiye on 14-16 November, 2019

Metode Applied Behavior Analysis Untuk Meningkatkan Kontak Mata Pada Anak Dengan Autism Spectrum Disorder

Kontak mata terjadi di awal perkembangan dan memiliki banyak fungsi bagi perkembangan kehidupan. Dengan adanya kontak mata dapat mengembangkan keterampilan yang lebih kompleks, seperti keterampilan sosial, kognitif, dan bahasa. Namun sejumlah besar anak dengan Autism Spectrum Disorder (ASD) gagal mengembangkan keterampilan kontak mata yang penting ini. Kegagalan anak ASD dalam mengembangkan kontak mata tersebut, menyebabkan aktivitasnya terganggu, baik dalam belajar maupun kehidupan sosial. Salah satu metode efektif digunakan untuk meningkatkan kontak mata anak dengan ASD adalah Applied Behavior Analysis (ABA). Metode tersebut terstruktur, mudah diukur, dan didesain khusus bagi anak dengan spectrum autis. Tujuan dari penelitian ini untuk mengetahui adakah pengaruh Metode ABA dalam meningkatkan kontak mata anak dengan ASD. Penelitian ini menggunakan metode eksperimen Single Case Experimental Design dengan desain A-B. Subjek dari penelitian ini yaitu seorang anak laki-laki berusia 7 tahun dengan ASD kategori sedang. Alat ukur dalam penelitian ini menggunakan checklist. Analisis data statistik yang digunakan dalam penelitian ini adalah analisis dengan grafik. Hasil penelitian menunjukkan metode ABA dapat meningkatkan kontak mata anak dengan ASD

Economic Token To Improve On-Task Behavior In Children With Attention Deficit Hyperactivity Disorder

Memusatkan perhatian pada tugas atau perilaku on-task merupakan salah satu keterbatasan yang dimiliki oleh anak dengan Attention Deficit Hyperactivity Disorder. Pada sisi lain, perilaku on-task ini seharusnya dimiliki oleh anak usia sekolah. Tujuan dari penelitian ini adalah meningkatkan durasi perilaku on-task pada anak dengan gangguan Attention Deficit Hyperactivity Disorder dengan menggunakan teknik token ekonomi. Desain eksperimen yang digunakan adalah single subject design model A-B-A. Intervensi dilakukan selama 10 kali pertemuan. Partisipan yang digunakan dalam penelitian ini adalah anak dengan gangguan Attention Deficit Hyperactivity Disorder yang berusia 9 tahun 9 bulan. Pengumpulan data dilakukan menggunakan observasi pada durasi perilaku on-task anak ketika mengerjakan tugas. Analisis data menggunakan analisis grafik. Efek dari intervensi dapat dilihat dari grafik durasi perilaku on-task yang diukur pada fase baseline 1, fase intervensi dan fase baseline 2. Hasil penelitian menemukan bahwa teknik token ekonomi mampu meningkatkan durasi perilaku on-task pada anak dengan Attention Deficit Hyperactivity Disorder