Achievement goal orientation profiles and performance in a programming MOOC

It has been suggested that performance goals focused on appearing talented (appearance goals) and those focused on outperforming others (normative goals) have different consequences, for example, regarding performance. Accordingly, applying this distinction into appearance and normative goals alongside mastery goals, this study explores what kinds of achievement goal orientation profiles are identified among over 2000 students participating in an introductory programming MOOC. Using Two-Step cluster analysis, five distinct motivational profiles are identified. Course performance and demographics of students with different goal orientation profiles are mostly similar. Students with Combined Mastery and Performance Goals perform slightly better than students with Low Goals. The observations are largely in line with previous studies conducted in different contexts. The differentiation of appearance and normative performance goals seemed to yield meaningful motivational profiles, but further studies are needed to establish their relevance and investigate whether this information can be used to improve teaching.Peer reviewe

Tavoiteorientaatioprofiilit ja suoriutuminen ohjelmoinnin MOOC-kurssilla

Tavoitteet. Valtaosa tietojenkäsittelytieteen kontekstissa tehdystä tavoiteorientaatiotutkimuksesta on ollut muuttujalähtöistä. Tämän tutkielman tavoitteena oli syventää ymmärrystä tietojenkäsittelytieteen opiskelijoista ja saavutusmotivaatiosta henkilösuuntautunutta lähestymistapaa käyttäen. Eri tavoiteorientaatioiden välistä vuorovaikutusta tarkasteltiin tunnistamalla yleisimmät tavoiteorientaatioprofiilit ja tutkimalla niiden välisiä eroja suoriutumisessa. Toisin kuin aiemmissa henkilösuuntautunutta lähestymistapaa hyödyntävissä tutkimuksissa, ryhmittely-muuttujina käytettiin oppimisorientaation lisäksi suoritusorientaatiota jaoteltuna tarkemmin tavoitteisiin päihittää toiset (normative goal) ja vaikuttaa pätevältä (appearance goal). Menetelmät. Tutkimukseen osallistui 2059 avoimen internet-pohjaisen ohjelmoinnin alkeiskurssin opiskelijaa. Aineisto kerättiin kyselylomakkeella, automaattisesti arvioiduista ohjelmointitehtävistä ja loppukokeesta. Tavoiteorientaatiomittarin rakennetta tarkasteltiin eksploratiivisella faktorianalyysillä (EFA). Opiskelijat luokiteltiin ryhmiin tavoiteorientaatioiden perusteella TwoStep-klusterianalyysia käyttäen. Profiilien ominaispiirteitä ja eroja suoriutumisessa tutkittiin ristiintaulukointien ja varianssianalyysien (ANOVA) avulla. Tulokset ja johtopäätökset. Tavoiteorientaatioprofiileja tunnistettiin viisi: Saavutusorientoituneet (31,2%), Suoritusorientoituneet (18,9%), Oppimis- ja suoritusorientoituneet (18,0%), Vähäisesti motivoituneet (17,6%) ja Oppimisorientoituneet (14,3%). Oppimis- ja suoritusorientoituneiden opiskelijoiden suoriutuminen oli kahden mittarin osalta tilastollisesti merkitsevästi parempaa kuin Vähäisesti motivoituneiden opiskelijoiden. Aiempien tutkimusten tapaan tuloksissa korostuu useampaan tavoitteeseen pyrkimisen ja suoriutumisen välinen positiivinen yhteys. Lisää tutkimusta tarvitaan tavoiteorientaatioprofiilien ja muiden koulutukseen liittyvien tulosten yhteyksien selvittämiseen ohjelmoinnin opetuksen kontekstissa. Tämänkaltaista tietoa voidaan hyödyntää uusia oppimisinterventioita ja kursseja suunniteltaessa. Tähän tutkielmaan perustuva artikkeli ‘Achievement Goal Orientation Profiles and Performance in a Programming MOOC’ tullaan esittelemään ITiCSE 2020 -konferenssissa ja julkaisemaan konferenssijulkaisussa.Aims. In the context of computing education, the vast majority of prior research examining achievement goal orientations has been conducted using variable-centred methods. In order to deepen understanding of the student population and achievement motivation, this Master’s Thesis employed person-oriented perspectives. The interplay of different goal orientations was explored by identifying prevalent motivational profiles and investigating profile differences in performance. Normative and appearance performance goals were handled as separate clustering variables in addition to mastery goals for the first time. Methods. The participants were 2059 introductory programming MOOC students. Data were collected by a questionnaire and from automatically assessed programming assignments and final exam. An exploratory factor analysis (EFA) was conducted for the achievement goal orientation items to examine the factor structure. Using TwoStep cluster analysis, the students were classified into clusters according to their achievement goal orientations. Cross tabulations and analyses of variance (ANOVA) were conducted to investigate profile characteristics and differences in performance. Results and Conclusions. Five distinct achievement goal orientation profiles were identified: Approach-Oriented (31.2%), Performance-Oriented (18.9%), Combined Mastery and Performance Goals (18.0%), Low Goals (17.6.%) and Mastery-Oriented (14.3.%). Students with Combined Mastery and Performance Goals performed significantly better than students with Low Goals regarding two metrics. Consistent with previous findings, the results highlight the positive link between multiple goal pursuit and performance. Further studies are needed to investigate motivational profiles in relation to other educational outcomes in the context of computing education. This kind of knowledge is valuable for designing interventions and new courses. The article ‘Achievement Goal Orientation Profiles and Performance in a Programming MOOC’, which is based on the present thesis, will be presented at ITiCSE 2020 (Conference on Innovation and Technology in Computer Science Education) conference and published in conference proceedings

Analysing self-regulated learning strategies of MOOC learners through self-reported data

Massive open online courses (MOOCs) require registered learners to be autonomous in their learning. Nevertheless, prior research studies showed that many learners lack the necessary self-regulated learning (SRL) skills to succeed in MOOCs. This research study aimed to gain insights into the relationships that exist between SRL and background information from MOOC learners. To this end, a series of three MOOCs on computer programming offered through edX were used to collect self-reported data from learners using an adaptation of the Motivated Strategies for Learning Questionnaire. Results show significant differences in general learning strategies and motivation by continent, prior computing experience and percentage of completed MOOCs. Men reported higher motivation than women, whereas pre-university learners needed further guidance to improve their learning strategies.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, InnovaT and PROF-XXI (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP). This publication reflects the views only of the authors, and funders cannot be held responsible for any use which may be made of the information contained therein

Understanding Learners' Motivation and Learning Strategies in MOOCs

MOOCs (Massive Open Online Courses) have changed the way in which OER (Open Educational Resources) are bundled by teachers and consumed by learners. MOOCs represent an evolution towards the production and offering of structured quality OER. Many institutions that were initially reluctant to providing OER have, however, joined the MOOC wave. Nevertheless, MOOCs detractors strongly criticize their high dropout rates. The dropout rate is a commonly accepted metric of success for traditional education, but it may not be as suitable when dealing with OER, in general, and with MOOCs, in particular, since learners' motivations to take a course are very diverse, and certain self -regulated learning strategies are required to tackle the lack of personalized tutoring and keep pace in the course . This paper presents an empirical study on the motivation and learning strategies of MOOC learners. Six thousand three hundred and thirty-five learners from 160 countries answered a self report 7- point Likert-type questionnaire based on the Motivated Strategies for Learning Questionnaire (MSLQ) as part of a MOOC titled Introduction to Programming with Java. Results indicate that learners were highly motivated and confident to do well in the course. Learning strategies, however, can be improved, especially regarding time management.This work has been co-funded by the Erasmus+ Programme of the European Union, projects MOOC-Maker (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), SHEILA (562080-EPP-1-2015-BE-EPPKA3-PI-FORWARD) and COMPETEN-SEA (574212-EPP-1-2016-1- NL-EPPKA2-CBHE-JP), by the Madrid Regional Government, through the eMadrid Excellence Network (S2013/ICE-2715), and by the Spanish Ministry of Economy and Competitiveness, project RESET (TIN2014-53199-C3-1-R) and fellowships FPDI-2013-17411 and PTQ-15-07505

Learners expectations and motivations using content analysis in a MOOC

The phenomenon of massive open online courses (MOOCs) has transformed the online educational delivery of courses around the world. There are several literature on MOOC publicity in the press, but little has been mentioned and discussed about the learner expectation and motivation. This paper investigates MOOC learner expectations and motivation from different perspectives. What they are hoping to achieve and how they prefer to learn. Firstly, we review existing literature bringing findings about learner expectations and motivation. We provide discussion from previously analysed research to review some learners’ expectation leading to motivation. Secondly, using the initial pilot investigation, we provide preliminary analysis of data from computing for teachers MOOC, run by the University of Warwick, UK hosted using Moodle platform. The first pilot study of CfT MOOC registered over 500 participants in 2013/2014. The CfT MOOC is of two main strands, programming and computing concepts

Sentiment analysis in MOOCs: a case study

Proceeding of: 2018 IEEE Global Engineering Education Conference (EDUCON2018), 17-20 April, 2018, Santa Cruz de Tenerife, Canary Islands, Spain.Forum messages in MOOCs (Massive Open Online Courses) are the most important source of information about the social interactions happening in these courses. Forum messages can be analyzed to detect patterns and learners' behaviors. Particularly, sentiment analysis (e.g., classification in positive and negative messages) can be used as a first step for identifying complex emotions, such as excitement, frustration or boredom. The aim of this work is to compare different machine learning algorithms for sentiment analysis, using a real case study to check how the results can provide information about learners' emotions or patterns in the MOOC. Both supervised and unsupervised (lexicon-based) algorithms were used for the sentiment analysis. The best approaches found were Random Forest and one lexicon based method, which used dictionaries of words. The analysis of the case study also showed an evolution of the positivity over time with the best moment at the beginning of the course and the worst near the deadlines of peer-review assessments.This work has been co-funded by the Madrid Regional Government, through the eMadrid Excellence Network (S2013/ICE-2715), by the European Commission through Erasmus+ projects MOOC-Maker (561533-EPP-1-2015-1-ESEPPKA2-CBHE-JP), SHEILA (562080-EPP-1-2015-1-BEEPPKA3-PI-FORWARD), and LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and by the Spanish Ministry of Economy and Competitiveness, projects SNOLA (TIN2015-71669-REDT), RESET (TIN2014-53199-C3-1-R) and Smartlet (TIN2017-85179-C3-1-R). The latter is financed by the State Research Agency in Spain (AEI) and the European Regional Development Fund (FEDER). It has also been supported by the Spanish Ministry of Education, Culture and Sport, under a FPU fellowship (FPU016/00526).Publicad

Educational Theories and Learning Analytics : From Data to Knowledge

Under embargo until 17.01.21.acceptedVersio

Connecting Learner Motivation to Learner Progress and Completion in Massive Open Online Courses | Relier la motivation de l’apprenant à ses progrès et à l’achèvement des cours en ligne ouverts à tous

We examined how massive open online courses (MOOC) learners’ motivational factors, self-efficacy, and task-value related to their course progress and achievement, as informed by learners’ initial course completion intention. In three individual MOOCs, learners completed a pre-course survey to report their levels of task-value and self-efficacy and to indicate their intention to complete each course topic. Using clustering techniques, we identified two distinct groups of learners in the three MOOCs based on self-efficacy and task-value variables: higher-motivation group and lower-motivation group. The higher-motivation group achieved significantly higher grades in two of the MOOCs, and also adhered to their initial completion intention significantly more so than the lower-motivation group. We posit that MOOC completion research should consider learners’ topic-level interest as one success criterion. Further research can clarify perceived task-value in relation to learners’ existing knowledge, their learning goals, and learning outcomes related to the MOOC participation. Nous avons examiné comment, dans les cours en ligne ouverts à tous (CLOT), les facteurs de motivation des apprenants, leur autoefficacité et leur valeur tâche étaient reliés à leurs progrès et à leur achèvement du cours selon l’intention initiale d’achèvement du cours des apprenants. Dans trois CLOT, les apprenants ont rempli un sondage avant le début du cours pour indiquer leur degré de valeur tâche et d’autoefficacité, ainsi que leur intention de compléter chaque sujet du cours. À l’aide de techniques agglomératives, nous avons cerné deux groupes distincts d’apprenants dans trois CLOT selon les variables de la valeur tâche et de l’autoefficacité : un groupe à plus forte motivation, et un groupe dont la motivation était plus faible. Le groupe dont la motivation était plus élevée a obtenu des notes considérablement plus élevées dans deux CLOT et, dans deux cours, ont adhéré à leur intention initiale d’achèvement considérablement plus que le groupe dont la motivation était moindre. Nous posons en principe que la recherche sur l’achèvement des CLOT devrait tenir compte de l’intérêt des apprenants sur le plan des sujets comme étant un critère de réussite. De plus amples recherches pourraient clarifier la valeur tâche perçue relativement aux connaissances préalables des apprenants, à leurs objectifs d’apprentissage et aux résultats d’apprentissage liés à la participation aux CLOT

Influence of employer support for professional development on MOOCs enrolment and completion: Results from a cross-course survey

Although the potential of open education and MOOCs for professional development is usually recognized, it has not yet been explored extensively. How far employers support non-formal learning is still an open question. This paper presents the findings of a survey-based study which focuses on the influence of employer support for (general) professional development on employees’ use of MOOCs. Findings show that employers are usually unaware that their employees are participating in MOOCs. In addition, employer support for general professional development is positively associated with employees completing MOOCs and obtaining certificates for them. However, the relationship between employer support and MOOC enrollment is less clear: workers who have more support from their employers tend to enroll in either a low or a high number of MOOCs. Finally, the promotion of a minimum of ICT skills by employers is shown to be an effective way of encouraging employee participation in the open education ecosystem.JRC.J.3-Information Societ

Essays on the Design of Inclusive Learning in Massive Open Online Courses, and Implications for Educational Futures

This thesis examines the tensions and contradictions of Massive Open Online Courses (MOOCs) as a force for more inclusive tertiary education, particularly for adults without a college degree in the United States. Through a multimethodological research approach yielding three discrete papers, presented as chapters, this work seeks to augment and clarify the existing MOOCs literature across conceptual, quantitative, and qualitative domains. The first paper develops a conceptual framework, ‘hegemonic design bias,’ that describes the socio-technical development ecosystem in which MOOCs are embedded. This framework helps account for why MOOCs have yet to serve as a democratising force in education by highlighting the processes and constraints that bias MOOC production toward the already well-educated. The potential economic implications of these developments are also considered. The second paper provides insight into how underrepresented learners are engaging with entry-level MOOCs. The exploration of learning analytic data from an initial sample of more than 260,000 enrolees through cluster analysis and multinomial logistic regression indicates that students without a college degree are more likely to be high-performing learners compared to college-educated students in these courses. Additionally, students from approximated lower socioeconomic backgrounds are no less likely to be successful than students from approximated middle and higher socioeconomic backgrounds in these courses. The third paper provides insight into the opportunities and challenges producers face in building inclusive MOOCs through a qualitative analysis of six semi-structured interviews. The interviews unearthed diverse conceptions of inclusion among producers that reflect a sincere normative commitment to make inclusive MOOCs, though the conceptions were quite distinct and fragmented. Producers were intentional about utilising best-practice pedagogy, as well as innovative program design, to include diverse learners. Innovative technology partners helped create interactive, unique experiences, but this also led to challenges in harmonising the design process and required the considerable influence of intermediary actors. To conclude, I briefly consider the implications of these findings for research, practice, and policy, with particular attention to how the public and social sectors can incentivise improved design of MOOCs with the specific intent of helping adults without college degrees develop human capital in order to remain economically resilient amidst the disruptions of skills-biased technological change.Gates Cambridge Trus