323 research outputs found

    Online Instruction in Higher Education: Promising, Research-based, and Evidence-based Practices

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    The purpose of this study was to review the research literature on online learning to identify effective instructional practices. We narrowed our scope to empirical studies published 2013-2019 given that studies earlier than 2013 had become quickly outdated because of changes in online pedagogies and technologies. We also limited our search to studies with undergraduate and graduate students, application of an empirical methodological design, and descriptions of methodology, data analysis, and results with sufficient detail to assure verifiability of data collection and analysis. Our analysis of the patterns and trends in the corpus of 104 research studies led to identification of five themes: course design factors, student support, faculty pedagogy, student engagement, and student success factors. Most of the strategies with promising effectiveness in the online environment are the same ones that are considered to be effective in face-to-face classrooms including the use of multiple pedagogies and learning resources to address different student learning needs, high instructor presence, quality of faculty-student interaction, academic support outside of class, and promotion of classroom cohesion and trust. Unique to the online environment are user-friendly technology tools, orientation to online instruction, opportunities for synchronous class sessions, and incorporation of social media. Given the few studies utilizing methodological designs from which claims of causality can be made or meta-analyses could be conducted, we identified only faculty feedback as an evidence-based practice and no specific intervention that we could identify as research-based in online instruction

    Online Instruction in Higher Education: Promising, Research-based, and Evidence-based Practices

    Get PDF
    The purpose of this study was to review the research literature on online learning to identify effective instructional practices. We narrowed our scope to empirical studies published 2013-2019 given that studies earlier than 2013 had become quickly outdated because of changes in online pedagogies and technologies. We also limited our search to studies with undergraduate and graduate students, application of an empirical methodological design, and descriptions of methodology, data analysis, and results with sufficient detail to assure verifiability of data collection and analysis. Our analysis of the patterns and trends in the corpus of 104 research studies led to identification of five themes: course design factors, student support, faculty pedagogy, student engagement, and student success factors. Most of the strategies with promising effectiveness in the online environment are the same ones that are considered to be effective in face-to-face classrooms including the use of multiple pedagogies and learning resources to address different student learning needs, high instructor presence, quality of faculty-student interaction, academic support outside of class, and promotion of classroom cohesion and trust. Unique to the online environment are user-friendly technology tools, orientation to online instruction, opportunities for synchronous class sessions, and incorporation of social media. Given the few studies utilizing methodological designs from which claims of causality can be made or meta-analyses could be conducted, we identified only faculty feedback as an evidence-based practice and no specific intervention that we could identify as research-based in online instruction

    Video Augmentation in Education: in-context support for learners through prerequisite graphs

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    The field of education is experiencing a massive digitisation process that has been ongoing for the past decade. The role played by distance learning and Video-Based Learning, which is even more reinforced by the pandemic crisis, has become an established reality. However, the typical features of video consumption, such as sequential viewing and viewing time proportional to duration, often lead to sub-optimal conditions for the use of video lessons in the process of acquisition, retrieval and consolidation of learning contents. Video augmentation can prove to be an effective support to learners, allowing a more flexible exploration of contents, a better understanding of concepts and relationships between concepts and an optimization of time required for video consumption at different stages of the learning process. This thesis focuses therefore on the study of methods for: 1) enhancing video capabilities through video augmentation features; 2) extracting concept and relationships from video materials; 3) developing intelligent user interfaces based on the knowledge extracted. The main research goal is to understand to what extent video augmentation can improve the learning experience. This research goal inspired the design of EDURELL Framework, within which two applications were developed to enable the testing of augmented methods and their provision. The novelty of this work lies in using the knowledge within the video, without exploiting external materials, to exploit its educational potential. The enhancement of the user interface takes place through various support features among which in particular a map that progressively highlights the prerequisite relationships between the concepts as they are explained, i.e., following the advancement of the video. The proposed approach has been designed following a user-centered iterative approach and the results in terms of effect and impact on video comprehension and learning experience make a contribution to the research in this field

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

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    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

    Utilizing educational technology in computer science and programming courses : theory and practice

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    There is one thing the Computer Science Education researchers seem to agree: programming is a difficult skill to learn. Educational technology can potentially solve a number of difficulties associated with programming and computer science education by automating assessment, providing immediate feedback and by gamifying the learning process. Still, there are two very important issues to solve regarding the use of technology: what tools to use, and how to apply them? In this thesis, I present a model for successfully adapting educational technology to computer science and programming courses. The model is based on several years of studies conducted while developing and utilizing an exercise-based educational tool in various courses. The focus of the model is in improving student performance, measured by two easily quantifiable factors: the pass rate of the course and the average grade obtained from the course. The final model consists of five features that need to be considered in order to adapt technology effectively into a computer science course: active learning and continuous assessment, heterogeneous exercise types, electronic examination, tutorial-based learning, and continuous feedback cycle. Additionally, I recommend that student mentoring is provided and cognitive load of adapting the tools considered when applying the model. The features are classified as core components, supportive components or evaluation components based on their role in the complete model. Based on the results, it seems that adapting the complete model can increase the pass rate statistically significantly and provide higher grades when compared with a “traditional” programming course. The results also indicate that although adapting the model partially can create some improvements to the performance, all features are required for the full effect to take place. Naturally, there are some limits in the model. First, I do not consider it as the only possible model for adapting educational technology into programming or computer science courses. Second, there are various other factors in addition to students’ performance for creating a satisfying learning experience that need to be considered when refactoring courses. Still, the model presented can provide significantly better results, and as such, it works as a base for future improvements in computer science education.Ohjelmoinnin oppimisen vaikeus on yksi harvoja asioita, joista lähes kaikki tietojenkäsittelyn opetuksen tutkijat ovat jokseenkin yksimielisiä. Opetusteknologian avulla on mahdollista ratkaista useita ohjelmoinnin oppimiseen liittyviä ongelmia esimerkiksi hyödyntämällä automaattista arviointia, välitöntä palautetta ja pelillisyyttä. Teknologiaan liittyy kuitenkin kaksi olennaista kysymystä: mitä työkaluja käyttää ja miten ottaa ne kursseilla tehokkaasti käyttöön? Tässä väitöskirjassa esitellään malli opetusteknologian tehokkaaseen hyödyntämiseen tietojenkäsittelyn ja ohjelmoinnin kursseilla. Malli perustuu tehtäväpohjaisen oppimisjärjestelmän runsaan vuosikymmenen pituiseen kehitys- ja tutkimusprosessiin. Mallin painopiste on opiskelijoiden suoriutumisen parantamisessa. Tätä arvioidaan kahdella kvantitatiivisella mittarilla: kurssin läpäisyprosentilla ja arvosanojen keskiarvolla. Malli koostuu viidestä tekijästä, jotka on otettava huomioon tuotaessa opetusteknologiaa ohjelmoinnin kursseille. Näitä ovat aktiivinen oppiminen ja jatkuva arviointi, heterogeeniset tehtävätyypit, sähköinen tentti, tutoriaalipohjainen oppiminen sekä jatkuva palautesykli. Lisäksi opiskelijamentoroinnin järjestäminen kursseilla ja järjestelmän käyttöönottoon liittyvän kognitiivisen kuorman arviointi tukevat mallin käyttöä. Malliin liittyvät tekijät on tässä työssä lajiteltu kolmeen kategoriaan: ydinkomponentteihin, tukikomponentteihin ja arviontiin liittyviin komponentteihin. Tulosten perusteella vaikuttaa siltä, että mallin käyttöönotto parantaa kurssien läpäisyprosenttia tilastollisesti merkittävästi ja nostaa arvosanojen keskiarvoa ”perinteiseen” kurssimalliin verrattuna. Vaikka mallin yksittäistenkin ominaisuuksien käyttöönotto voi sinällään parantaa kurssin tuloksia, väitöskirjaan kuuluvien tutkimusten perusteella näyttää siltä, että parhaat tulokset saavutetaan ottamalla malli käyttöön kokonaisuudessaan. On selvää, että malli ei ratkaise kaikkia opetusteknologian käyttöönottoon liittyviä kysymyksiä. Ensinnäkään esitetyn mallin ei ole tarkoituskaan olla ainoa mahdollinen tapa hyödyntää opetusteknologiaa ohjelmoinnin ja tietojenkäsittelyn kursseilla. Toiseksi tyydyttävään oppimiskokemukseen liittyy opiskelijoiden suoriutumisen lisäksi paljon muitakin tekijöitä, jotka tulee huomioida kurssien uudelleensuunnittelussa. Esitetty malli mahdollistaa kuitenkin merkittävästi parempien tulosten saavuttamisen kursseilla ja tarjoaa sellaisena perustan entistä parempaan opetukseen

    The student-produced electronic portfolio in craft education

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    The authors studied primary school students’ experiences of using an electronic portfolio in their craft education over four years. A stimulated recall interview was applied to collect user experiences and qualitative content analysis to analyse the collected data. The results indicate that the electronic portfolio was experienced as a multipurpose tool to support learning. It makes the learning process visible and in that way helps focus on and improves the quality of learning. © ISLS.Peer reviewe

    Predicting Paid Certification in Massive Open Online Courses

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    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
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