Conference Paper

Attrition in MOOC: Lessons Learned from Drop-out Students

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Abstract

Despite the popularity of Massive Open Online Course (MOOC), recent studies have found that completion rates are low with some reported to be significantly lower than 10%. The low retention and completion rates are major concerns for educators and institutions. This paper investigates motivations for enrolling in a MOOC on the topic of ‘e-learning’ and discusses reasons for the attrition rates during the course. A survey of 134 students who had not completed the MOOC reveals that only 22% of the students had intended to complete the MOOC but was unable to due to various factors including academic and personal reasons. A big majority of the students indicated that changes in their job, insufficient time, difficulty with the subject matter and unchallenging activities are some of the reasons for the drop-out.

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... Some students are unable to retain their interest in the learning materials, and some view learning as a different form of experience in MOOCs [9]. In addition to the above points, researchers have indicated various challenges for continuance intention (initiating but not completing a MOOC), which is termed as unhealthy attrition [10]. First, this may include participants who are only interested in learning content or those who follow the activities and just participate selectively [7]. ...
... Therefore, educators have long struggled with high MOOC attrition rates. Learners who fail to accomplish a course are generally regarded as failure students, prompting plenty of studies into the factors that influence dropout or completion rates [10], [30]. In this regard, many researchers investigated students' profiles like demographic variables, personalization, commitment, attitudes, motivation, competence, selfefficacy, emotion, or prior experience. ...
... Thus, it can be affirmed that individual motivation is a significant predictor of MOOC's continuous intention. Finally, it is worth mentioning that a study [10] addressed the motivations of learners who had registered for MOOCs and whose initial desire was to complete the course but were non-completers because of various reasons. Difficulty in juggling work and study, poor course design, technical inability, and high interest in learning workload were significant obstacles that hindered the completion. ...
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The high dropout rate, lack of learners’ motivation, and MOOC users’ diversity are significant issues in MOOCs. In this sense, gamification is used to investigate if it can help to increase MOOC learners’ motivation and engagement, leading to a continued usage scenario. Further, this article aims to propose a theoretical model to identify the factors affecting MOOC learners’ continuance and empirically measure these factors. The model is based on the Expectation Confirmation Model (ECM) with additional constructs of motivation and gamification. Data is collected from 206 university students using an online survey. Structural Equation Modeling (SEM) is used for data analysis. The results show that motivation, satisfaction, and perceived usefulness influence continuous intention, with satisfaction being the most significant predictor (β = 0.373, p < 0.000). Motivation, confirmation, and perceived usefulness have a significant positive effect on satisfaction. Among the three gamification categories, achievement has the highest impact on motivation (β = 0.208, p < 0.001), followed by the social category (β = 0.1 43, p < 0.032). The effect of the immersion category is found to be non-significant. Based on the results, appropriate theoretical and practical implications are discussed.
... Η ίδια εικόνα παρουσιάζεται και στις έρευνες που αναλύθηκαν, όπου μετά την εγγραφή των ενδιαφερομένων στο εκάστοτε πρόγραμμα, κάποιοι δεν εμφανίζονται ποτέ, ειδικά όταν ο χρόνος που μεσολαβεί μέχρι την έναρξη των μαθημάτων είναι μεγάλος , κάποιοι συμμετέχουν ελάχιστα και κάποιοι άλλοι εγκαταλείπουν μέσα στις πρώτες εβδομάδες των μαθημάτων, ανεξάρτητα από το βαθμό της προηγούμενης συμμετοχής τους, έως ότου τελικά, υπάρξει μια σταθεροποίηση (Dillahunt, et al., 2014;Gütl, et al., 2014 Chan, & Lai, 2016;Crosslin, et al., 2017;Tawfik, et al., 2017). Υπάρχουν βέβαια και εξαιρέσεις, όπου οι εγγραφές συνεχίζονται και μετά το επίσημο τέλος του προγράμματος Perna, et al., 2014), αφού το μαθησιακό υλικό είναι προσβάσιμο, και μάλιστα, οι εγγραφές συνεχίζουν να αυξάνονται, παρά το γεγονός ότι δεν υπάρχει πλέον η δυνατότητα πιστοποίησης των γνώσεων που θα αποκτήσουν . ...
... Ο συνολικός χρόνος που αφιερώνει κάποιος ανά εβδομάδα και ο χώρος που επιλέγει να μελετήσει, εξαρτάται από τον διαθέσιμο ελεύθερο του χρόνο, από προσωπικούς παράγοντες Park, et al., 2015;Veletsianos, Collier, & Schneider, 2015), αλλά και από την ποιότητα του μαθησιακού υλικού (Veletsianos, et al., 2015). Για παράδειγμα, στην έρευνα των Gütl, et al. (2014), οι περισσότεροι εκπαιδευόμενοι επέλεξαν να διαβάσουν στη δουλειά, στο σπίτι μετά τη δουλειά, τα σαββατοκύριακα και κατά τη διάρκεια του γεύματος, αφιερώνοντας συνήθως 1-2 ώρες, ενώ πολύ λίγοι αφιέρωσαν 5 ή περισσότερες. ...
... Τα εμπόδια που αντιμετωπίζουν κατά τη διάρκεια των μαθημάτων και οδηγούν στην εγκατάλειψη τους είναι η έλλειψη χρόνου (Fini, 2009;Kop, et al., 2011;Cross, 2013;Zutshi, et al., 2013;Beaven, Codreanu, et al., 2014;Cassidy, et al., 2014;Gütl, et al., 2014;Nawrot & Doucet, 2014;Skrypnyk, et al., 2015;Zheng, et al., 2015;Veletsianos, et al., 2016;Kizilcec & Cohen, 2017;Shapiro, et al., 2017) και η καθυστέρηση στο χρονοδιάγραμμα τους, εξαιτίας άλλων υποχρεώσεων (Nawrot & Doucet, 2014;, η απουσία γνωστικού υπόβαθρου που θα επέτρεπε την κατανόηση των νέων πληροφοριών Gütl, et al., 2014;Park, et al., 2015;Shapiro, et al., 2017), η ποιότητα και η δυσκολία του μαθησιακού υλικού και των αξιολογήσεων Gütl, et al., 2014;Nawrot & Doucet, 2014;Park, et al., 2015;Whitehill, Williams, Lopez, Coleman, & Reich, 2015;Zheng, et al., 2015;Huang & Hew, 2016;Veletsianos, et al., 2016), ο σχεδιασμός των μαθημάτων Nawrot & Doucet, 2014;Park, et al., 2015) και του μαθησιακού περιβάλλοντος Tomkin & Charlevoix, 2014), η απουσία επίσημης αναγνώρισης των γνώσεων που αποκτούν Gamage, et al., 2015), η έλλειψη δεξιοτήτων στις νέες τεχνολογίες (Fini, 2009;Kop, et al., 2011;García, et al., 2015), η απουσία αλλά και η ποιότητα της ανατροφοδότησης/βοήθειας, είτε από τους άλλους εκπαιδευόμενους είτε από το εκπαιδευτικό και το βοηθητικό προσωπικό García, et al., 2015;Tomkin & Charlevoix, 2014;Park, et al., 2015), η έλλειψη επικοινωνίας με το εκπαιδευτικό προσωπικό Gütl, et al., 2014), η απουσία παρακίνησης από τρίτους , η απουσία της αίσθησης της κοινότητας Nawrot & Doucet, 2014;Zheng, et al., 2015) και της δυσκολίας να συνεργαστούν Κουτσοδήμου & Τζιμογιάννης, 2016), η χρήσης διαφορετικής γλώσσας και ζώνης ώρας (Fini, 2009;Park, et al., 2015), τεχνικά προβλήματα (Fini, 2009;Kop, et al., 2011;Gütl, et al., 2014;, η κούραση από τις καθημερινές υποχρεώσεις Veletsianos, et al., 2016;Kizilcec & Cohen, 2017;Shapiro, et al., 2017), προσωπικοί λόγοι και δυσκολίες Gütl, et al., 2014;Nawrot & Doucet, 2014;de Waard, et al., 2015;Woodgate, et al., 2015;Kizilcec & Cohen, 2017), κακή εμπειρία για το διδακτικό αντικείμενο (Shapiro, et al., 2017), δυσαρέσκεια για τη βαθμολόγηση (Tomkin & Charlevoix, 2014). Ενδέχεται όμως κάποιοι εκπαιδευόμενοι να εγκαταλείψουν το πρόγραμμα, όχι γιατί αντιμετώπισαν κάποια από τις παραπάνω δυσκολίες και εμπόδια, αλλά επειδή πέτυχαν το στόχο για τον οποίο συμμετείχαν, πριν την χρονική ολοκλήρωση του προγράμματος (Nawrot & Doucet, 2014;Whitehill, et al., 2015) ή γιατί συνειδητοποίησαν ότι δεν ήταν ό,τι περίμεναν Whitehill, et al., 2015). ...
Thesis
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Το Σεπτέμβριο του 2015, η UNESCO διαμόρφωσε την παγκόσμια ατζέντα με τους στόχους για την αειφόρο ανάπτυξη, που θα πρέπει να υλοποιηθούν μέχρι το 2030. Όσον αφορά στην εκπαίδευση, αναγνωρίστηκε η σημασία της για την κοινωνική και οικονομική ανάπτυξη των λαών, ενώ τέθηκε ο στόχος της διασφάλισης της δίκαιης και χωρίς αποκλεισμούς ποιότητας της εκπαίδευσης και της προώθησης ευκαιριών δια βίου εκπαίδευσης για όλους. Αυτός ο στόχος φαίνεται ότι μπορεί να υλοποιηθεί με τα Ανοικτά Μαζικά Διαδικτυακά Μαθήματα ή MOOCs, όπως είναι ευρέως γνωστά, καθώς προσφέρει τριτοβάθμιου επιπέδου εκπαίδευση σε εκατομμύρια ανθρώπους, σε όλο τον κόσμο. Παρά ταύτα, ένα μεγάλο ποσοστό όσων εγγράφονται στα μαθήματα δεν τα ολοκληρώνουν ποτέ, εμφανίζοντας πολύ μικρά ποσοστά ολοκλήρωσης που κυμαίνονται από 5-15%. Σκοπός της παρούσας διατριβής είναι να διερευνηθεί κατά πόσο ο εκπαιδευτικός σχεδιασμός, η γενικότερη οργάνωση του προγράμματος και η χρήση της μικροεφαρμογής που αναπτύχθηκε (MCII+), μπορεί να βοηθήσει τους εκπαιδευόμενους να αναπτύξουν το βαθμό της αυτορρύθμισής τους επιτυγχάνοντας υψηλές επιδόσεις και μεγαλύτερα ποσοστά ολοκλήρωσης από τα καθιερωμένα. Για το λόγο αυτό σχεδιάστηκε και υλοποιήθηκε επιμορφωτικό πρόγραμμα MOOC με θέμα την «Ενδοσχολική βία και τον εκφοβισμό» το οποίο φιλοξενήθηκε σε πλατφόρμα OpenEdx που εγκαταστήσαμε και παραμετροποιήσαμε σε virtual server του Πανεπιστημίου Αιγαίου . Το επιμορφωτικό πρόγραμμα διάρκειας 8 εβδομάδων, και κατ’ επέκταση και η έρευνα, διεξήχθη από τις 3/2 έως τις 29/3 του 2020. Στο πρόγραμμα συμμετείχαν, κυρίως, εκπαιδευτικοί από όλες τις βαθμίδες εκπαίδευσης, αλλά και στελέχη εκπαίδευσης, φοιτητές και ιδιώτες από όλη την Ελλάδα. Τα ερευνητικά ερωτήματα αφορούσαν τη διερεύνηση των παραγόντων που επηρεάζουν την αυτορρύθμιση των εκπαιδευομένων πριν την εγγραφή τους στο πρόγραμμα, κατά τη διάρκειά του και αφού ολοκληρωθεί. Τέθηκαν, επίσης, ερωτήματα σχετικά με τη συμβολή του εκπαιδευτικού υλικού στην κινητοποίηση των εκπαιδευομένων. Για τη συλλογή των ερευνητικών δεδομένων χρησιμοποιήθηκαν τρία διαφορετικά ερωτηματολόγια που απαντήθηκαν σε τρεις φάσεις του προγράμματος, αρχή, μέσο και λήξη, καθώς και δεδομένα από την ίδια την πλατφόρμα των μαθημάτων (επίδοση), την υπηρεσία Google Analytics (επισκεψιμότητα) και τη μικροεφαρμογή που ενσωματώθηκε στην πλατφόρμα των μαθημάτων και χρησιμοποιήθηκε από τη μία ερευνητική ομάδα (Πειραματική). Τα ποσοτικά και ποιοτικά ερευνητικά δεδομένα που αναλύθηκαν, έδειξαν μια γενική αποδοχή του εκπαιδευτικού σχεδιασμού του προγράμματος, του εκπαιδευτικού του υλικού και του τρόπου που υλοποιήθηκε, συμβάλλοντας στην αυτορρύθμιση όλων των συμμετεχόντων, επιτυγχάνοντας υψηλές επιδόσεις και ποσοστά επιτυχούς ολοκλήρωσης του, ανεξαρτήτως ερευνητικής ομάδας (Ελέγχου, Πειραματικής) στην οποία ανήκαν. Περισσότερο, όμως, βοηθήθηκε, ως προς την αυτορρύθμισή της, η Πειραματική ομάδα η οποία χρησιμοποίησε την ερευνητική εφαρμογή MCII+ που τους υποβοήθησε να εφαρμόσουν την αυτορρυθμιστική στρατηγική τη Ψυχικής αντίθεσης με προθέσεις υλοποίησης (Mental Contrasting with Implementation Intentions-MCII), καθώς και μια σειρά άλλων αυτορρυθμιστικών διεργασιών της 1ης (Πρόνοια) και 3ης (Αναστοχασμός) φάσης του μοντέλου αυτορρύθμισης του Zimmerman.
... Globally, completion rates range from 5-15% (Jordan, 2013). The obstacles that the learners face during the courses and lead to their abandonment are lack of time (Fini, 2009;Kop, Fournier, & Mak, 2011;Belanger & Thornton, 2013;Cross, 2013;Grainger, 2013;Zutshi, O'Hare, & Rodafinos, 2013;Beaven, Codreanu, & Creuzé, 2014;Cassidy, Breakwell, & Bailey, 2014;Gütl, Rizzardini, Chang, & Morales, 2014;Nawrot & Doucet, 2014;Schulze, 2014;Kizilcec & Halawa, 2015;Skrypnyk, de Vries, & Hennis, 2015;Zheng, Rosson, Shih, & Carroll, 2015;Veletsianos, Reich, & Pasquini, 2016;Kizilcec & Cohen, 2017;Shapiro, et al., 2017) and the delay in their schedule due to other obligations (Nawrot & Doucet, 2014;Kizilcec & Halawa, 2015), the absence of a cognitive background that would allow the understanding of new information (Belanger & Thornton, 2013;Gütl, et al., 2014;Park, Jung, & Reeves, 2015;Shapiro, et al., 2017), the quality and difficulty of learning material and assessments (Belanger & Thornton, 2013;Gütl, et al., 2014;Nawrot & Doucet, 2014;Schulze, 2014;Park, et al., 2015;Skrypnyk, et al., 2015;Whitehill, Williams, Lopez, Coleman, & Reich, 2015;Zheng, et al., 2015;Huang & Hew, 2016;Veletsianos, et al., 2016), the course design (Gütl, et al., 2014;Nawrot & Doucet, 2014;Park, et al., 2015), the awareness of the absence of formal recognition of their knowledge (Schulze, 2014;Gamage, Fernando, & Perera, 2015), the absence but also the quality of feedback/assistance either from other learners or from teaching and support staff (Gütl, et al., 2014;Schulze, 2014;García, Tenorio, & Ramírez, 2015;Tomkin & Charlevoix, 2014;Park, et al., 2015), the lack of communication with teaching staff (Kop, et al., 2011;Gütl, et al., 2014), lack of motivation from third parties (Gütl, et al., 2014), the absence of a sense of community (Gütl, et al., 2014;Nawrot & Doucet, 2014;Zheng, et al., 2015) and the difficulty of collaborating (Zutshi, et al., 2013;Koutsodimou & Tzimogiannis, 2016). However, some learners may leave the program, not because they faced any of the above difficulties and obstacles, but because they achieved the goal for which they participated, before the completion of the program (Nawrot & Doucet, 2014;Schulze, 2014;Kizilcec & Halawa, 2015;Whitehill, et al., 2015) or why they realized that the program did not meet their needs (Schulze, 2014;Whitehill, et al., 2015). ...
... Globally, completion rates range from 5-15% (Jordan, 2013). The obstacles that the learners face during the courses and lead to their abandonment are lack of time (Fini, 2009;Kop, Fournier, & Mak, 2011;Belanger & Thornton, 2013;Cross, 2013;Grainger, 2013;Zutshi, O'Hare, & Rodafinos, 2013;Beaven, Codreanu, & Creuzé, 2014;Cassidy, Breakwell, & Bailey, 2014;Gütl, Rizzardini, Chang, & Morales, 2014;Nawrot & Doucet, 2014;Schulze, 2014;Kizilcec & Halawa, 2015;Skrypnyk, de Vries, & Hennis, 2015;Zheng, Rosson, Shih, & Carroll, 2015;Veletsianos, Reich, & Pasquini, 2016;Kizilcec & Cohen, 2017;Shapiro, et al., 2017) and the delay in their schedule due to other obligations (Nawrot & Doucet, 2014;Kizilcec & Halawa, 2015), the absence of a cognitive background that would allow the understanding of new information (Belanger & Thornton, 2013;Gütl, et al., 2014;Park, Jung, & Reeves, 2015;Shapiro, et al., 2017), the quality and difficulty of learning material and assessments (Belanger & Thornton, 2013;Gütl, et al., 2014;Nawrot & Doucet, 2014;Schulze, 2014;Park, et al., 2015;Skrypnyk, et al., 2015;Whitehill, Williams, Lopez, Coleman, & Reich, 2015;Zheng, et al., 2015;Huang & Hew, 2016;Veletsianos, et al., 2016), the course design (Gütl, et al., 2014;Nawrot & Doucet, 2014;Park, et al., 2015), the awareness of the absence of formal recognition of their knowledge (Schulze, 2014;Gamage, Fernando, & Perera, 2015), the absence but also the quality of feedback/assistance either from other learners or from teaching and support staff (Gütl, et al., 2014;Schulze, 2014;García, Tenorio, & Ramírez, 2015;Tomkin & Charlevoix, 2014;Park, et al., 2015), the lack of communication with teaching staff (Kop, et al., 2011;Gütl, et al., 2014), lack of motivation from third parties (Gütl, et al., 2014), the absence of a sense of community (Gütl, et al., 2014;Nawrot & Doucet, 2014;Zheng, et al., 2015) and the difficulty of collaborating (Zutshi, et al., 2013;Koutsodimou & Tzimogiannis, 2016). However, some learners may leave the program, not because they faced any of the above difficulties and obstacles, but because they achieved the goal for which they participated, before the completion of the program (Nawrot & Doucet, 2014;Schulze, 2014;Kizilcec & Halawa, 2015;Whitehill, et al., 2015) or why they realized that the program did not meet their needs (Schulze, 2014;Whitehill, et al., 2015). ...
... Globally, completion rates range from 5-15% (Jordan, 2013). The obstacles that the learners face during the courses and lead to their abandonment are lack of time (Fini, 2009;Kop, Fournier, & Mak, 2011;Belanger & Thornton, 2013;Cross, 2013;Grainger, 2013;Zutshi, O'Hare, & Rodafinos, 2013;Beaven, Codreanu, & Creuzé, 2014;Cassidy, Breakwell, & Bailey, 2014;Gütl, Rizzardini, Chang, & Morales, 2014;Nawrot & Doucet, 2014;Schulze, 2014;Kizilcec & Halawa, 2015;Skrypnyk, de Vries, & Hennis, 2015;Zheng, Rosson, Shih, & Carroll, 2015;Veletsianos, Reich, & Pasquini, 2016;Kizilcec & Cohen, 2017;Shapiro, et al., 2017) and the delay in their schedule due to other obligations (Nawrot & Doucet, 2014;Kizilcec & Halawa, 2015), the absence of a cognitive background that would allow the understanding of new information (Belanger & Thornton, 2013;Gütl, et al., 2014;Park, Jung, & Reeves, 2015;Shapiro, et al., 2017), the quality and difficulty of learning material and assessments (Belanger & Thornton, 2013;Gütl, et al., 2014;Nawrot & Doucet, 2014;Schulze, 2014;Park, et al., 2015;Skrypnyk, et al., 2015;Whitehill, Williams, Lopez, Coleman, & Reich, 2015;Zheng, et al., 2015;Huang & Hew, 2016;Veletsianos, et al., 2016), the course design (Gütl, et al., 2014;Nawrot & Doucet, 2014;Park, et al., 2015), the awareness of the absence of formal recognition of their knowledge (Schulze, 2014;Gamage, Fernando, & Perera, 2015), the absence but also the quality of feedback/assistance either from other learners or from teaching and support staff (Gütl, et al., 2014;Schulze, 2014;García, Tenorio, & Ramírez, 2015;Tomkin & Charlevoix, 2014;Park, et al., 2015), the lack of communication with teaching staff (Kop, et al., 2011;Gütl, et al., 2014), lack of motivation from third parties (Gütl, et al., 2014), the absence of a sense of community (Gütl, et al., 2014;Nawrot & Doucet, 2014;Zheng, et al., 2015) and the difficulty of collaborating (Zutshi, et al., 2013;Koutsodimou & Tzimogiannis, 2016). However, some learners may leave the program, not because they faced any of the above difficulties and obstacles, but because they achieved the goal for which they participated, before the completion of the program (Nawrot & Doucet, 2014;Schulze, 2014;Kizilcec & Halawa, 2015;Whitehill, et al., 2015) or why they realized that the program did not meet their needs (Schulze, 2014;Whitehill, et al., 2015). ...
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Twelve years after the advent of MOOCs, the University of the Aegean (Greece) implemented its first MOOC on “Violence and bullying in schools”, in which about 2,000 people showed interest in attending. Eventually, 1309 people started it and 1050 (80.21%) completed it successfully, achieving high performance. The present work, which is part of the doctoral research of the first researcher, outlines the participation of the learners in the program and the obstacles they encountered during it while identifying the reasons for its high completion rate with high performance. The results showed that mainly the quality of the instructional material, the instructional design of the program, and its organization, as well as the timely support provided to learners, contributed significantly to the successful completion of the program achieving high performance. These findings can be considered by future MOOC program designers, in order to design and implement programs that meet the requirements and facilitate the participation of those who attend. Δώδεκα χρόνια μετά την εμφάνιση των MOOCs, το Πανεπιστήμιο Αιγαίου υλοποίησε το πρώτο του MOOC με θέμα την Ενδοσχολική βία και τον εκφοβισμό, στο οποίο εκδήλωσαν ενδιαφέρον για να το παρακολουθήσουν περίπου 2000 άτομα. Τελικά, το ξεκίνησαν 1309 άτομα και το ολοκλήρωσαν επιτυχώς 1050 (80,21%), πετυχαίνοντας υψηλές επιδόσεις. Η παρούσα εργασία, που αποτελεί τμήμα της διδακτορικής έρευνας του πρώτου ερευνητή, σκιαγραφεί τη συμμετοχή των εκπαιδευομένων στο πρόγραμμα και τα εμπόδια που αντιμετώπισαν κατά τη διάρκειά του, ενώ εντοπίζει τους λόγους του υψηλού ποσοστού ολοκλήρωσης του με υψηλές επιδόσεις. Τα αποτελέσματα έδειξαν ότι κυρίως η ποιότητα του εκπαιδευτικού υλικού, ο εκπαιδευτικός σχεδιασμός του προγράμματος και η οργάνωσή του, καθώς και η έγκαιρη υποστήριξη που παρεχόταν στους εκπαιδευόμενους, συνέβαλαν σημαντικά στην επίτευξη των συγκεκριμένων αποτελεσμάτων. Τα ευρήματα αυτά, μπορούν να ληφθούν υπόψη από τους σχεδιαστές μελλοντικών προγραμμάτων MOOCs, ώστε να σχεδιάζουν και να υλοποιούν προγράμματα που θα ικανοποιούν τις απαιτήσεις και θα διευκολύνουν τη συμμετοχή, όσων τα παρακολουθούν. Article visualizations: </p
... From previous studies, online learning platforms (such as MOOCs), found on a large scale, have collected huge amounts of learners' behavioral data, but have not innovated in-course design or restructured platform architecture based on the data, and have failed to solve some difficult problems of online education (Ross et al., 2014). Scholars have pointed out that although some online courses have been adjusted based on research, the video completion rate of courses on online platforms is still far below that of traditional courses (Gütl et al., 2014;Evans et al., 2016). ...
... Moreover, students who easily acquire knowledge when teachers speak at a low speed show reduced self-efficiency of learning and a lack of learning motivation and enthusiasm. This presumption conforms to the conclusion of scholars that "slowing down the video speed reduces learners' satisfaction" based on empirical research (Gütl et al., 2014). According to the Yerkes-Dodson law, the learning effect would be low under a lower level of motivation in easier tasks (Broadhurst, 1959;Sari, 2020). ...
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Following the COVID-19 pandemic, online learning has become a new mode of learning that students must adapt to. However, the mechanisms by which students receive and grasp knowledge in the online learning mode remain unknown. Cognitive load theory (CLT) offers instructions to students considering the knowledge of human cognition. Therefore, this study considers the CLT to explore the internal mechanism of learning under the online mode in an experimental study. We recruited 76 undergraduates and randomly assigned them to four groups in which they will watch videos at four different kinds of speed (1.0× or 1.25× or 1.5× or 2× speed). The study observed and analyzed how video playback speed affected students' learning and cognitive load to obtain the following results: (1) Video playback speed significantly influenced the students' learning effect. The best effect was observed at the speed of 1.25× and 1.5×. (2) The speed that affected the learning effect best differed according to the students' learning abilities. High-level group students performed best at the speed of 1.5×, whereas low-level group students performed best at the speed of 1.25×. (3) The 1.5× speed showed significant differences in the learning effect by students' majors. This indicates that the cognitive load of liberal arts students increased greatly at this speed. (4) A change in playback speed has a significant impact on the cognitive load. Accelerated playback speed increases the cognitive load of students. The highest learning effect is observed under medium cognitive load.
... En cuanto a la tasa global de finalización (13.35%), aunque escasa, es alta respecto a las tasas detalladas en la literatura que hablan de tasas de finalización de menos del 10% y un promedio alrededor del 7% (Jordan, 2014). Como causas de deserción es importante distinguir entre causas "saludables" (como falta de tiempo, problemas laborales, etc.) y "no saludables" -imputables al mal diseño del curso o al poco apoyo prestado desde el mismo, es decir, a una mala experiencia de aprendizaje y una falta de personalizacioń (Gütl, Hernández, Chang y Morales, 2014;MOOC-Maker, 2016). Por ello un modelo pedagógico que fomente la creación de comunidades virtuales, con actividades variadas y complejas y con feedback constante es, sin duda, un factor que puede asegurar que más participantes finalicen, aunque no debemos olvidar que las motivaciones de los estudiantes son muy diversas y no siempre pasan por terminar el curso. ...
... La evaluación debe ser una herramienta fundamental en la construcción de un diseño sometido a permanente revisión y mejora, también en los nuevos espacios formativos virtuales. El diseño pedagógico debe incluir un apoyo adecuado del equipo docente al alumnado y una retroalimentación e interacción suficientes que eviten una sensación de aislamiento y desconexión (Gütl et al., 2014), ayudando a contrarrestar algunas de las causas del abandono. La utilización de las redes sociales en los sMOOCs facilita esta tarea. ...
Article
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Resumen:La evaluación en los Cursos Online Masivos Abiertos (COMA -en inglés MOOC-) es en la actualidad un elemento central de debate y por ello uno de los aspectos de mayor preocupación a la hora de proponer su diseño pedagógico. El objetivo de este artículo es describir el sistema de evaluación del curso MOOC “Alfabetización Digital para colectivos en riesgo de exclusión social: Estrategias para la Intervención europeo Socioeducativa” del Proyecto ECO (implementado durante tres ediciones) y diagnosticar sus fortalezas y debilidades. Hemos diseñado el modelo evaluativo como una parte central y dinámica del curso, que nos permitiera conocer el perfil de nuestros participantes virtuales (evaluación inicial), tutorizar su trabajo y valorar sus resultados (evaluación de los aprendizajes) y, por último, obtener la máxima información posible de cara a aprender de este tipo de procesos formativos y tomar decisiones de mejora en cada nueva edición (evaluación orientada a la mejora). Presentamos y analizamos los principales datos obtenidos con las herramientas de recogida de información utilizadas (cuestionario inicial, interacciones en grupos de Facebook y otros espacios sociales, cuestionarios autoevaluativos, evaluación por pares, informantes clave y datos de la plataforma). Para finalizar, se discuten los resultados con la intención de aportar reflexiones de nuestra experiencia que puedan servir de guía a la hora de diseñar modelos de evaluación en MOOC. La principal tesis que sostenemos es que el modelo evaluativo es un elemento central del diseño que debe perfilarse desde el minuto uno y que debe integrarse plenamente con el resto de los elementos del diseño pedagógico, contemplando estrategias para la evaluación inicial y formativa de los participantes y del propio proceso de enseñanza-aprendizaje y no sólo estar basado en la evaluación final orientada a la certificación.
... Generally online courses are reported to have significantly higher dropout rates in comparison to contact teaching [26,38,49] with MOOCs regularly having a dropout rate of over 80% [54] or even over 90% [19]. SPOCs have generally better retention rates, but there is much variance in SPOC withdrawal depending on how they are organized [27,36]. ...
... The overall dropout rate in the UNIPS module Becoming a Teacher (38,1 %) was observed to be low compared to the typical dropout rates in MOOCs [26,38,49,19] which have dropout rates as high as 95%. This, however, is closer to that of typical SPOCs (Kaplan and Haenlein, 2016) and as UNIPS modules were organised at specific dates to a limited amount of students, they resemble SPOCs more than MOOCs in that sense, even if the courses were offered to participants from many universities. ...
Chapter
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Online education provides learning opportunities to a global audience. Most popular MOOC platforms have millions of users and MOOC designers are already competing with each other on how to spark and retain the interest of students. However, currently in popular MOOCs, roughly 90% of enrolled students yield their participation and previous research has identified that the dropouts occur mostly in the very early stages of the courses. This study explores student retention and engagement in pedagogical online courses aimed for university staff members and doctoral students, with quantitative data (N=404) collected between the years 2016-2019. In addition, this study looks at differences in dropout rates between students of different age, gender, teaching position and department. Based on the conducted statistical analysis, age, gender, teaching position or department have no significant correlation with dropout rates. The majority of participants who drop out from the courses do so in the beginning without completing a single task. University teachers and doctoral students behave in online courses similarly as other students, and the results of the current study fits well with predictions from previous studies. However, this study found two anomalies: (1) A relatively low dropout rate (38,1%) and (2) Over 22% of students yielding their participation return to the courses (n=31) after which over 50% of them complete the courses. The results highlight the importance of the beginning of online courses for reducing the overall dropout rates and suggest that students yielding their participation are likely to complete the courses the second time, if they enroll again.
... Given the large numbers of courses (most of them with very high ratings) and information out there, learners are eas-ily disoriented by the information overload (Zhang et al., 2018), and they usually find difficulties choosing which is the appropriate course for them (taking several days to decide); this course selection difficulty is also related to the high dropout rate as they may not be choosing the best course to fit their preferences. Some of the reasons of the high dropout rate reported previously could be related with the overload and misunderstandings that learners suffer, since they do not usually know the real characteristics of the course that they are choosing: in a survey conducted by Gütl et al. (2014), 8.96% of the students reported that the course was too difficult, meanwhile 7.46% emphasized that the course was not challenging. Moreover, 14.93% of students also highlighted that the learning environment was not personalized, and another 6.72% indicated that the courses were poorly taught. ...
... Furthermore, we can take a look at both of our topic models and discuss the coherence and usability of the topics revealed. Regarding the qualitative description model, we see that some topics could be really useful for students to identify the courses' key features, such as topic 14 ("slide," "powerpoint," "lack," "repetitive") identifying courses that might be not as well designed as other, or topic 2 ("personal," "authentic," productive") highlighting the personalization that many students missed in other courses (Gütl et al., 2014). However, we also identify other topics that might not add useful information, such as topic 11 ("beginner," "intermediate," "advanced") showing keywords that are confusing. ...
Preprint
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The recent pandemic has changed the way we see education. It is not surprising that children and college students are not the only ones using online education. Millions of adults have signed up for online classes and courses during last years, and MOOC providers, such as Coursera or edX, are reporting millions of new users signing up in their platforms. However, students do face some challenges when choosing courses. Though online review systems are standard among many verticals, no standardized or fully decentralized review systems exist in the MOOC ecosystem. In this vein, we believe that there is an opportunity to leverage available open MOOC reviews in order to build simpler and more transparent reviewing systems, allowing users to really identify the best courses out there. Specifically, in our research we analyze 2.4 million reviews (which is the largest MOOC reviews dataset used until now) from five different platforms in order to determine the following: (1) if the numeric ratings provide discriminant information to learners, (2) if NLP-driven sentiment analysis on textual reviews could provide valuable information to learners, (3) if we can leverage NLP-driven topic finding techniques to infer themes that could be important for learners, and (4) if we can use these models to effectively characterize MOOCs based on the open reviews. Results show that numeric ratings are clearly biased (63\% of them are 5-star ratings), and the topic modeling reveals some interesting topics related with course advertisements, the real applicability, or the difficulty of the different courses. We expect our study to shed some light on the area and promote a more transparent approach in online education reviews, which are becoming more and more popular as we enter the post-pandemic era.
... To date, extant research on MOOCs have mainly focused on understanding student motivation for enrolling in MOOCs, as well as the reasons for student drop out (Gütl, Rizzardini, Chang, & Morales, 2014;Hew & Cheung, 2014;Zheng, Rosson, Shih, & Carroll, 2015), instructors' reasons for offering MOOCs, as well as the challenges involved (Hew & Cheung, 2014), student behavior in discussion forums (Huang, Dasgupta, Ghosh, Manning, Sanders, 2014;Mak, Williams, & Mackness, 2010), the challenges of peer assessment (Piech, Huang, Chen, Do, Ng, & Koller, 2013), and predicting student performance or dropout using statistical methods (Adamopoulos, 2013;Coetzee et al., 2014;de barba, Kennedy, & Ainley, 2016;Kizilcec, Piech, & Schneider, 2013;Kloft, Stiehler, Zheng, & Pinkwart, 2014). ...
Conference Paper
Student opinions play a very important role in education-it can influence student behaviors such as whether to pay attention or not, which in turn influences student decision to drop out or to continue learning. This study offers a new contribution by using sentiment analysis, otherwise known as opinion mining, technique to analyze and classify sentiments found in a largescale corpus of reflective sentences (75,239 sentences) posted by 18,032 students who completed one or more of 218 MOOCs. We employed the open-sourced text processing package TextBlob 1 as the sentiment analysis engine, which computes text sentiment by averaging the term sentiments of the text based on a sentiment dictionary derived from WordNet3. We explored and described the students' positive and negative sentiments with respect to one or more of the following six aspects: (a) structure and pace, (b) video, (c) instructor, (d) content and resources, (e) interaction and support, and (f) assignment and assessment.
... This, however, often leads to trainings and courses that follow one-fits-all strategies [49,54,26] and do not take into account the different qualifications, needs and learning goals of their participants. And indeed, the measurable success of MOOCs has been rather limited so far; depending on the source, completion rates have been reported to often lie between five and fifteen percent only [60,5,29,37]. The reasons for drop-out are diverse, ranging from insufficient time allocation, difficulties with the subject matter or unchallenging activities to disorganization and insufficient planning [29, 37, for example]. ...
Preprint
The goal of this paper is to introduce a new framework for fast and effective knowledge state assessments in the context of personalized, skill-based online learning. We use knowledge state networks - specific neural networks trained on assessment data of previous learners - to predict the full knowledge state of other learners from only partial information about their skills. In combination with a matching assessment strategy for asking discriminative questions we demonstrate that our approach leads to a significant speed-up of the assessment process - in terms of the necessary number of assessment questions - in comparison to standard assessment designs. In practice, the presented methods enable personalized, skill-based online learning also for skill ontologies of very fine granularity without deteriorating the associated learning experience by a lengthy assessment process.
... To illustrate, Google Analytics reported that the bounce rate for the book in this time period (or the number of users who navigated away after viewing only one page) was 71.85% with the average user session lasting less than 3 minutes. This is why, for instance, MOOCs have such notoriously low completion rates (Gütl et al., 2014;Rivard, 2013) and why when studying open environments and resources it makes sense to limit analyses to users whose behaviors suggest an intent to participate in the behaviors we are measuring (e.g., Veletsianos et al., 2021). Judging by user scrolling behaviors, time on page, textual length, and chapter text complexity for the target textbook, it is estimated that only about 22.7% of page views actually constituted a "read" of the contents, and among those who read the contents, there was no incentive or prodding to complete the end-of-chapter survey. ...
... Despite the popularity of Massive Open Online Courses (MOOCs), studies have found that a lot of MOOCs had low completion rates that were less than 10% (Fidalgo-Blanco, Sein-Echaluce, & García-Peñalvo, 2016;Gütl, Rizzardini, Chang, & Morales, 2014;Joo, So, & Kim, 2018). For the past few years, many researchers have been trying to find potential factors that may explain the low engagement and low completion rates in MOOCs. ...
Article
Research has repeatedly proven the importance of social interactions in online learning contexts such as Massive Open Online Courses (MOOCs), where learners often reported isolation and a lack of peer support. Previous studies of social presence suggested that the ways learners present themselves socially online affect their learning outcomes. In order to further understand the role of learners' social presence, this study attempts to examine the relationship between social presence and learners' prestige in the learner network of a MOOC. An automated text classification model based on the latest machine learning techniques was developed to identify different social presence indicators from forum posts, while two metrics (in-degree and authority score) in social network analysis (SNA) were used to measure learners' prestige in the learner network. Results revealed that certain social presence indicators such as Asking questions, Expressing gratitude, Self-disclosure, Sharing resources and Using Vocatives have positive correlations with learners' prestige, while the expressions of Disagreement/doubts/criticism and Negative emotions were counterproductive to learners' prestige. The findings not only reinforce the importance of social presence in online learning, but also shed light on the strategies of leveraging social presence to improve individual's prestige in social learning contexts like MOOCs.
... Although some of the students enroll in the online course just to obtain learning materials, some students interacted with a learning environment, i.e. listened to the lectures, read additional materials, tried quizzes and assignments, and did not obtain enough points to obtain a certificate of accomplishment. Reasons for failing the course can be insufficient background knowledge, lack of time, course design, or one felt discouraged, frustrated, or bored [17]. ...
Article
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Online learning environments became popular in recent years. Due to high attrition rates, the problem of student dropouts became of immense importance for course designers, and course makers. In this paper, we utilized lasso and ridge logistic regression to create a prediction model for dropout on the Open University database. We investigated how early dropout can be predicted, and why dropouts occur. To answer the first question, we created models for eight different time frames, ranging from the beginning of the course to the mid-term. There are two results based on two definitions of dropout. Results show that at the beginning AUC of the prediction model is 0.549 and 0.661 and rises to 0.681 and 0.869 at mid-term. By analyzing logistic regression coefficients, we showed that at the beginning of the course demographic features of the student and course description features are the most important variables for dropout prediction, while later student activity gains more importance.
... The results confirm that the participation in and completion of the MOOC was higher compared to that of previous studies that showed big dropout rates in other MOOCs [32,33]. The MOOC topic contributed to this high completion rates. ...
Article
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Background Clinical training during the COVID-19 pandemic is high risk for medical students. Medical schools in low- and middle-income countries (LMIC) have limited capacity to develop resources in the face of rapidly developing health emergencies. Here, a free Massive Open Online Course (MOOC) was developed as a COVID-19 resource for medical students working in these settings, and its effectiveness was evaluated. Methods The RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework was utilized to evaluate the effectiveness of MOOC in teaching medical students about COVID-19. The data sources included the student registration forms, metrics quantifying their interactions within the modules, students’ course feedback, and free-text responses. The data were collected from the Moodle learning management system and Google analytics from May 9 to September 15, 2020. The research team analyzed the quantitative data descriptively and the qualitative data thematically. Results Among the 16,237 unique visitors who accessed the course, only 6031 medical students from 71 medical schools registered, and about 4993 (83% of registrants) completed the course, indicating high levels of satisfaction (M = 8.17, SD = 1.49) on a 10-point scale. The mean scores of each assessment modules were > 90%. The free-text responses from 987 unique students revealed a total of 17 themes (e.g., knowing the general information on COVID-19, process management of the pandemic in public health, online platform use, and instructional design) across the elements of the RE-AIM framework. Mainly, the students characterized the MOOC as well-organized and effective. Conclusions Medical students learned about COVID-19 using a self-paced and unmonitored MOOC. MOOCs could play a vital role in the dissemination of accurate information to medical students in LMIC in future public health emergencies. The students were interested in using similar MOOCs in the future.
... One way to increase the motivation of learners to continue and complete the program in which they participate, is its instructional design, as various studies have reported that poor instructional design is an obstacle that leads to dropping out of courses (Gütl, Rizzardini, Chang, & Morales, 2014;Nawrot & Doucet, 2014;Loizzo, Ertmer, Watson, & Watson, 2017), while on the contrary, a good instructional design can promote learning (Yousef, Chatti, Schroeder, & Wosnitza, 2014;Jung, Kim, Yoon, Park, & Oakley, 2019). Similarly, the instructional material of a MOOC program that is created or selected during the instructional design, is an important factor that influences the participation of the learners. ...
... Recently, e-Learning researchers also confirmed that one of the biggest limitations of MOOCs (even with other course types) is the rate of course completion, it is so low (about 2 ~ 5%), and one way of the overcome solutions is solving the problem of course design (Yuan & Powell, 2013) (Gütl et al., 2014) (Jacobsen, 2017). ...
Conference Paper
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Course design is one of the most exciting topics in the e-Learning field. Up to now in Vietnam, there have not been many completely methodical types of research in the course design for the open education field, especially in which pedagogical script design is at an essential significant problem. The article proposes methodical progress of designing a pedagogical script. It aims to be a framework for building short-term online courses of the retraining and in-service training programs in Vietnam.
... MOOCs offer ease of completion, as the student can take the course remotely, without the need to travel to a university. However, the number of dropouts of these courses is even higher than those in classroom courses [22,23]; therefore, more recent studies have focused on the prediction of dropout in MOOCs [24][25][26], with few studies focused on the prediction of dropout in face-to-face courses [1]. The lack of studies focused on in-person courses is mainly due to the difficulty of data collection from face-to-face courses, with few universities having organized systems that easily allow data collection, making the work of researchers complicated. ...
Article
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School dropout permeates various teaching modalities and has generated social, economic, political, and academic damage to those involved in the educational process. Evasion data in higher education courses show the pessimistic scenario of fragility that configures education, mainly in underdeveloped countries. In this context, this paper presents an Internet of Things (IoT) framework for predicting dropout using machine learning methods such as Decision Tree, Logistic Regression, Support Vector Machine, K-nearest neighbors, Multilayer perceptron, and Deep Learning based on socioeconomic data. With the use of socioeconomic data, it is possible to identify in the act of pre-registration who are the students likely to evade, since this information is filled in the pre-registration form. This paper proposes the automation of the prediction process by a method capable of obtaining information that would be difficult and time consuming for humans to obtain, contributing to a more accurate prediction. With the advent of IoT, it is possible to create a highly efficient and flexible tool for improving management and service-related issues, which can provide a prediction of dropout of new students entering higher-level courses, allowing personalized follow-up to students to reverse a possible dropout. The approach was validated by analyzing the accuracy, F1 score, recall, and precision parameters. The results showed that the developed system obtained 99.34% accuracy, 99.34% F1 score, 100% recall, and 98.69% precision using Decision Tree. Thus, the developed system presents itself as a viable option for use in universities to predict students likely to leave university.
... An empirical study revealed that the majority of students perceived that multimedia elements, animation, video, and online forums are among the core features that attain their interest in MOOC (Gütl et al., 2014). A group of researchers suggested that students should be given the flexibility of learning at their own pace in MOOC as resolve the low completion rate problem (Jacqmin, 2019;Lee et al., 2019). ...
Article
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Data structure is a foundation subject for computer science students in Malaysian universities. To fully grasp the subject, students are required to do a lot of exercises. Many students, however, are lacking the motivation to either carry out the exercises or to actively participate in the teaching and learning process. As a result, they find it difficult to internalize the concepts and master the programming skills. Consequently, students tend to perceive data structure as a difficult subject. The objective of this paper is to present the implementation of Massive Open Online Course (MOOC) in teaching the subject of Fundamentals of Data Structures. It was anticipated that the MOOC would facilitate the students in self-regulated learning (SRL), thus increasing their motivation and participation. The MOOC has been used by the Diploma of Computer Science students in Universiti Teknologi MARA, Perak Branch from September to December 2019. The students’ perception on MOOC and its effect to six SRL attributes has been collected through an online survey at the end of academic semester. The result was very encouraging as it shows that the MOOC has contributed positively to the students’ SRL notably in the area the self-defined goal setting, self-efficacy, self-interest and self-strategies. (PDF) Self-regulated learning with massive open online course (MOOC) for the fundamentals of data structure course: A descriptive analysis. Available from: https://www.researchgate.net/publication/343381853_Self-regulated_learning_with_massive_open_online_course_MOOC_for_the_fundamentals_of_data_structure_course_A_descriptive_analysis [accessed Aug 05 2020].
... Ένας τρόπος αύξησης των κινήτρων των εκπαιδευομένων, ώστε να συνεχίσουν και να ολοκληρώσουν το πρόγραμμα στο οποίο συμμετέχουν, αποτελεί ο εκπαιδευτικός του σχεδιασμός, καθώς σε διάφορες έρευνες έχει αναφερθεί ότι ο κακός σχεδιασμός του προγράμματος αποτελεί εμπόδιο που οδηγεί στην εγκατάλειψη των μαθημάτων (Gütl, Rizzardini, Chang, & Morales, 2014;Nawrot & Doucet, 2014;Loizzo, Ertmer, Watson, & Watson, 2017), ενώ αντίθετα ο καλός σχεδιασμός τους σχετίζεται θετικά με την ολοκλήρωση (Jung, Kim, Yoon, Park, & Oakley, 2018). Γενικά, στα προγράμματα MOOCs υπάρχουν περιθώρια βελτίωσης του εκπαιδευτικού τους σχεδιασμού (Park, Jung, & Reeves 2015). ...
Article
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Ένα από τα μεγαλύτερα προβλήματα των MOOCs αποτελεί ο μικρός αριθμός εκπαιδευομένων που τελικά τα ολοκληρώνουν. Ανάμεσα στους λόγους εγκατάλειψης αποτελούν τόσο ο εκπαιδευτικός σχεδιασμός του προγράμματος, όσο και το εκπαιδευτικό του υλικό. Έρευνες έχουν δείξει ότι και τα δύο μπορούν να συμβάλουν στην αύξηση των κινήτρων των εκπαιδευομένων και στη βελτίωση της αυτορρύθμισής τους. Στην παρούσα εργασία που αποτελεί τμήμα της διδακτορικής έρευνας του πρώτου ερευνητή, αξιοποιώντας το Ερωτηματολόγιο Παρώθησης Εκπαιδευτικού Υλικού (IMMS), παρουσιάζονται τα αποτελέσματα της κινητοποίησης των εκπαιδευομένων που συμμετείχαν στο πρώτο MOOC του Πανεπιστημίου Αιγαίου με θέμα την «Ενδοσχολική βία και τον εκφοβισμό». Τα αποτελέσματα επιβεβαιώνουν ότι ο εκπαιδευτικός σχεδιασμός και το εκπαιδευτικό υλικό, μπορούν να έχουν θετική επίδραση στην κινητοποίηση των εκπαιδευομένων βοηθώντας τους να ολοκληρώσουν επιτυχώς το πρόγραμμα, επιτυγχάνοντας υψηλές επιδόσεις. Τα ευρήματα αυτά μπορούν να φανούν χρήσιμα σε σχεδιαστές εκπαιδευτικών προγραμμάτων MOOCs.
... In this study, we were only able to administer the questionnaire at the end of the course and therefore the vast majority of respondents were individuals who had already or were about to complete it. This prevented us from addressing the issue of attritionone of the main MOOC-related research topics (Deshpande & Chukhlomin, 2017;Gütl et al., 2014). However, given the high percentage of enrolled students who completed the course, we would suggest that our approach is of great interest to understand how motivations vary in individuals who effectively succeed in following a MOOC. ...
Article
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In recent years, MOOCs have become firmly established as valid e-learning environments and, as such, have been developed by many universities using different types of platform. Given the voluntary nature of MOOC enrolment, motivation is crucial to our understanding of why students register for and complete these courses. The present study explores the motivations that characterize MOOC participants and how they relate to technology acceptance variables (data collected via questionnaires) and participation variables (observational data collected via the platform). Our results indicate that students show exceptionally high levels of intrinsic motivation. However, extrinsic motivation also plays a relevant role, suggesting that the two are not mutually exclusive. Although only intrinsic motivation appears to be systematically associated with differences in technology acceptance, both are associated with differences in participation, but in contrasting ways. Our results provide insights that will enable us to improve MOOC design in order to enhance participant satisfaction, particularly when different sources of motivation are involved. Future research based on the modeling of technology acceptance and participation will also benefit from this study.
... The results have been compared with other nearby or related official UNED degrees, as well as some of the students' opinions and their results. Through the course, the students were able to distance themselves from everyday life in order to self-observe the professional concerns and tasks and personal realities that occupy them and that are in line with the experiences of other authors [27][28][29][30]. This educational framework, together with the need to promote online teaching at the university in times of uncertainty because of the situation generated by the COVID-19 pandemic, has led us to favor autonomy in the online learning process and to promote the MOOC, "Socio-sanitary and Social Services in Social Work". ...
Article
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This exploratory study is part of the training and innovation project (GID2016-16) of the National University of Distance Education (UNED) in Spain. The current socioeconomic and educational contexts derived from COVID-19 has led university institutions to develop methodological innovations in the teaching-learning process. Among these strengthening measures are the MOOCs, the most appropriate strategy to bring students closer to new digital platforms that favor the acquisition of knowledge. A methodological pluralism, combining quantitative and qualitative perspectives, has been used. The main results of the descriptive analysis compare the data of students enrolled in the MOOC, “Social and Health Care and Social Services in Social Work”, and other related bachelor’s and master’s degree courses during the four years analyzed. One of the conclusions is the emerging possibility of offering university studies that are more in line with the current teleworking market. The development of online methodologies favors the democratization of education, reaching the student body as a whole and universalizing content and learning. Among the main conclusions, it is worth highlighting the degree of satisfaction shown by the students who took advantage of the MOOC, and the training opportunities afforded by MOOC courses, during the time of COVID-19.
... Massive open online courses, or MOOCs, have the potential to reach a wider and less affluent audience than a typical college classroom. MOOCs have notoriously low completion rates though, with one study reporting only 10% of students completing the course (Gütl, Rizzardini, Chang, & Morales, 2014). More generally, universities have increasingly implemented "flipped classrooms" in which content learning is done primarily outside the classroom (often through video lectures) and activities and projects are completed inside the classroom (Bergmann & Sams, 2012). ...
Chapter
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Digital technologies have changed the everyday use of human memory. When information is saved or made readily available online, there is less need to encode or maintain access to that information within the biological structures of memory. People increasingly depend on the Internet and various digital devices to learn and remember, but the implications and consequences of this dependence remain largely unknown. The present chapter provides an overview of research to date on memory in the digital age. It focuses in particular on issues related to transactive memory, cognitive offloading, photo taking, social media use, and learning in the classroom.
... Nghiên cứu tập trung trên các khóa học trực tuyến ngắn hạn dạng MOOC nhằm phản ánh quan niệm thiết kế e-Learning hướng đến xu thế "chuyển tải" một lượng tri thức "vừa đủ" của một môn học/học phần hay một chuyên đề học tập giúp người học phát triển hoặc nâng cao một/vài năng lực cụ thể nào đó trong một thời lượng học phù hợp. Một trong những hạn chế lớn nhất của MOOCs là tỉ lệ hoàn thành khóa học rất thấp (2~5%) (Yang et al., 2013;Gütl et al., 2014). Vì vậy, việc cải tiến tính "hiệu quả" và "hấp dẫn" của các khóa học trực tuyến ngắn hạn là bài toán thu hút nhiều sự quan tâm đối với các chuyên gia e-Learning (Wang et al., 2010). ...
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Recently, building short-term online courses for the various training programs, such as soft-skills courses, professional development courses, free open courses, is necessary. There have been many approaches to designing online courses based on research and experiences of domestic/foreign institutions. However limited studies have been conducted to share knowledge of mapping learning outcomes for short-term online programs. The article proposes three types of matrices that are used to design short-term online courses and a maping process with seven steps. At each step of the process, concepts, related guidelines, and formatted templates are presented. The research results are some online courses designed based on learning outcomes and their matrices, these courses have been evaluated and implemented in practice with many positive feedbacks from learners.
... Less structured online learning environments are associated with difficulties in self-regulating learning (Broadbent & Poon, 2015), while self-regulated learning is a positive factor influencing learners' behavior in MOOCs (Kizilcec et al., 2017;Lee et al., 2019;Littlejohn et al., 2016). Further, the low completion rates of MOOCs are partly related to low level of self-regulated learning (Gütl et al., 2014;Kizilcec & Halawa, 2015;Yu et al., 2019). Thus, to improve completion rates of courses, Web 2.0 and serious games for course success (Aparicio et al., 2019), big data for dropout prediction (Liang et al., 2016), machine learning, and other technologies are introduced. ...
Chapter
As Massive Open Online Courses (MOOCs) address huge numbers of participants, they also incorporate a high availability of trace data that can be used for learning analytics (LA). But MOOCs also face high rates of dropout, which LA might offer additional support via learning dashboards. To improve the design of dashboards for supporting self-regulated learning, the Community of Inquiry (CoI) model emphasizing three elements (teaching presence, cognitive presence, and social presence) for online learning experiences was used in this chapter. Thirty-seven indicators related to the three elements of the CoI model were used in this study as a basis for analyzing MOOCs and their learning dashboards. The sample contains 20 courses on 5 MOOC platforms (three English, two Chinese) and 2 subjects (education and computer science). Findings indicated that all CoI indicators were present in courses, but only six indicators (TP1b, TP1d, TP3b, TP3e, CP1d, CP4b) appeared on the related learning dashboards. Furthermore, education courses used case studies and peer assignments more frequently, while Chinese and English MOOCs have some differences in course design. Considering that the dashboards analyzed entail simple visualizations and low personalization, MOOC participants might not be provided with sufficient support. Based on the findings, it is recommended to use, for example, dashboards including individual learning goals, more pedagogical guidance in discussion forum, and the recommendation of learning groups or communities. Upcoming research needs to investigate why MOOCs do not make sufficient use of learning dashboards and how to combine learning analytics, MOOCs, and learning theories.
... A study compiled by EDX shows that 17% of the enrolled learners consulted the course and only 8% completed their certification, this means that majority of the enrolled students do not complete their course [21]. Therefore, the issue of attrition in MOOC and the factors contributing to it, has been focus of many studies [22][23][24]. One such factor may be information overload, as the growing number MOOC platforms and courses they offer [15] and consequently the learner is mostly overwhelmed with information overload [25]. ...
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Online learning environments (OLE) are gaining popularity, including learning management systems (LMS) and massive open online courses (MOOCs), which are the best modern alternate solutions available for education in the current era. The luxury to learn irrespective of geographical and temporal restrictions makes it an attractive resource. At the start of 2020, the global pandemic enforced social distance practice worldwide, changing the work environment dynamics, leaving the people with options like online trading, work from home, and online education. The online learning environments gained particular attention in the educational sector, where users could access the online learning resources to fulfil their academic requirements during the lockdown. From massively available content such as MOOC, the learners are overwhelmed with the available choices. In this scenario, recommender systems (RS) come to the rescue to help the learner make appropriate choices for completing the enrolled course. There is tremendous scope and a multitude of opportunities available for researchers to focus on this domain. An exhaustive analysis is required to spotlight the opportunities in this realm. Various studies have been performed to provide such solutions in multiple areas of the MOOC recommendation systems (MOOCRS) such as course recommendation, learner peer recommendation, resource recommendations, to name a few. This is a compendious study into the research conducted in this area, identifying 670 articles out of 116 selected for analysis published from 2013 to 2021. It also highlights multiple areas in MOOC, where the recommendation is required, as well as technologies used by other researchers to provide solutions over time.
... Since more activity is considered positive, disengagement is often viewed negatively, as it may point to issues of poor usability, waning attention, disinterest and negative emotional experiences. Yet, it may also signal that user needs have been momentarily satisfied (O'Brien & Toms, 2008); this latter point about disengagementthat it can be the result of a successful interactionis often missed (cf., Gütl, Rizzardini, Chang, & Morales, 2014). Another perspective is that disengagement is a means to deal with problematic technology use, e.g., "quitting" a social media site because we spend too much time using it. ...
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User engagement has become a much-cited construct in human-computer interaction (HCI) design and evaluation research and practice. Constructed as a positive and desirable outcome of users' interactions, more frequent and longer interactions are considered evidence of engagement. Disengagement, when discussed, is considered a best avoided outcome of technology use or a solution to problematic technology use. In the case of the former, disengagement may signal usability issues or user disinterest, while the latter emphasizes that some engaging interactions can result in negative consequences (e.g., addiction) for end-users. In this paper, we draw upon examples from HCI research and digital tools to present a more nuanced understanding of the symbiotic relationship between engagement and disengagement in order to propose a new definition and novel ways to model disengagement. Further, we challenge generalizations that dichotomize engagement (positive, continuous, accompanied by high interactivity and beneficial to end-users) and disengagement (negative, stopping use or detrimental use) and invite readers to interpret engagement in the context of desirability with respect to users' goals and perceived agency. We concluded with implications that invite the reader to make space for disengagement and move beyond usage data in the evaluation of engagement. This paper is a call to step away from the practice of engagement-for-engagement's sake, and to reflect on whether and when engagement is meaningful and desirable for end users.
... The pedagogical design and learning regularities in MOOCs have drawn increasing attention in the interdisciplinary field of educational technology. Due to the openness and autonomy of MOOCs, however, students' low completion rates in MOOCs have been primary concerns for educators and institutions (Alraimi et al., 2015;Gütl et al., 2014;Clow, 2013;Yang et al., 2013). The lack of active interaction, teachers' inadequate guidance, and especially low cognitive level may the key factors leading to low completion rates in MOOCs, while interactive discussion in the forum plays an important role in facilitating interaction and cognition of learners (Khalil & Ebner, 2014;Ferrari et al., 2020;Xiong et al., 2015). ...
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The MOOCs (Massive Open Online Courses) forum carries rich discussion data that contains multi-level cognition-related behavior patterns, which brings the potential for an in-depth investigation into the development trend of the group and individual cognitive presence in discourse interaction. This paper describes a study conducted in the context of an introductory astronomy course on the Chinese MOOCs platform , examining the relationship between discussion pacings (i.e., instructor-paced or learner-paced discussion), cognitive presence, and learning achievements. Using content analysis, lag sequential analysis, logistic regression, and grouped regression approaches, the study analysed the online discussion data collected from the Astronomy Talk course involving 2603 participants who contributed 24,018 posts. The findings of the study demonstrated the significant cognitive sequential patterns, and revealed the significant differences in the distribution of cognitive presence with different discussion pacings and learning achievement groups, respectively. Moreover , we found that the high-achieving learners were mostly in the exploration, integration , and resolution phase, and learner-paced discussion had a greater moderating effect on the relationship between cognitive presence and learning achievements. Based on the findings and discussion, suggestions for improving the learners' cogni-tive presence and learning achievements in the MOOC environment are discussed.
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Massive Open Online Courses (MOOCs) have democratized access to higher education, but low entry and exit barriers make dropout rates in these courses much higher than in traditional in-person courses. Previous research has explored the main factors driving student dropout and proposed predictive models to identify students at risk. However, it is still unclear what type of interventions should be implemented to effectively reduce the dropout rate in MOOCs. We use findings from the literature to propose a theoretical framework to guide the design of interventions to reduce dropout in MOOCs. We design four interventions and implement them using A/B testing and natural experiment approaches in courses of the MITx MicroMasters® Program in Supply Chain Management (a MOOC-based program). Our findings reveal that ad hoc interventions communicated via email are not effective in reducing dropout, but interventions that modify course contents to make them more approachable for students show a positive impact on dropout reduction. By proposing a theoretical framework and uncovering the design features that can help to create effective interventions for dropout reduction, we provide a foundation for further research in the area of dropout interventions. This paper offers guidance to MOOC designers and instructors on how to improve student engagement and increase completion rates.
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Los sesgos de elección al rescate de la retención en los MOOCs 423 Resumen: Luego del éxito de los MOOCS en los últimos años, la baja retención, pone en duda su efectividad. La presente investigación analiza los datos de diferentes MOOCs con los objetivos de determinar los estudiantes y MOOCs con perfiles desertores y encontrar patrones de estudiantes finalizadores, a través de distorsiones de la realidad (sesgos). Se utilizó la técnica de estratificación y predicción, árbol de decisión de tipo CHAID (Chi-square automatic interaction detector). Los resultados indican que las variables interés por el certificado, sesgos de elección y edad son las que mejor predicen los perfiles de los estudiantes desertores. Para el caso de los perfiles de los cursos que favorecen la deserción; la duración del MOOC, los sesgos de elección, la cantidad de módulos y el número de profesores muestran el curso con mayor probabilidad de abandono. Los mayores predictores en el interés el certificado final se encuentra descritos por los estudiantes con estudios de licenciatura y del área de interés de negocios. Contrario a lo esperado, se encontró como mayor predictor de la deserción el número incremental de preguntas a lo largo de las diferentes evaluaciones durante el MOOC. La discusión presenta estrategias pedagógicas que benefician directamente la supervivencia de los MOOCs. Palabras claves: CHAID, deserción, Sesgo minucia, MOOC, certificado Los sesgos de elección al rescate de la retención en los MOOCs Biases election to rescue retention in MOOCs VOL.24, Nº3 (Noviembre,2020)
Chapter
Conversational pedagogical agents offer a wide range of possibilities when incorporated into virtual training courses. In the context of Massive Open Online Courses (MOOC), the interaction with the students must scale, and usually there is no human interaction, so a good configuration of conversational pedagogical agents provides a potential to provide personalized attention. It is relevant to highlight that there are no “one-size-fits-all” approaches in terms of conversational pedagogical agents given that from the moment of starting the conversation, it usually starts from scratch, without much user context. This fact is very relevant when talking about scalability, since in MOOC, there are many students in different states and a similar approach is not useful for all, so the processing must start with a previous context. It is here that the use of Learning Analytics provides a better context for decision-making, initial values to launch the model, and greater possibility of success. The purpose of this chapter is to present a prototype integrating a conversational agent embedded in the instructional activities of a MOOC. The aim is to increase motivation and student’s engagement to achieve their learning goals. The expectation is to produce improvements in students’ behavior and higher completion rates.
Chapter
MOOCs (Massive Open Online Courses) have gained popularity for e-learning purposes. Effectiveness of a MOOC depends on the platform interface design and management, which should create student cohesiveness and optimize collaboration. A MOOC prototype was developed and e-learning applications were pilot-tested for one semester with a total of 160 students from graduate courses in a French business school. Students used a mobile supported e-learning environment and reported their experiences through the writing of a synthesis, the building of a CMS (Content Management System) and the elaboration of a content curation system.
Chapter
Student attrition is one of the most frequently stated problems with massive open online courses. Although there is a growing body of research that investigates the factors leading to withdrawing from a course, there is also a need for data-driven solutions for early detection as a means to take remedial action. In this case study, we examine the use of several data preprocessing techniques to model attrition on the basis of students’ interactions with course materials and resources. Data for this study were obtained using the Open University Learning Analytics Dataset (OULAD), enabling the analysis of daily summaries of clickstream data. The data were segmented using a variable-sized overlapping window to take into account assignment submission dates as a context-sensitive factor for student attrition. In each sliding time window, features were extracted to characterize student interactions with curricular materials and converted to a set of linearly uncorrelated principal components. The analysis demonstrates that relatively accurate detection of the likelihood of students dropping out from a course can be attained within approximately 10 weeks from the course start date or before completion of assignments worth 20% of the final grade. Although a decision tree model outperformed alternative approaches to model student attrition, a log-linear model performed comparably well on the sparse representation of student interactions obtained through principal components analysis. We discuss the implications of these findings for early identification of at-risk students and the design of analytics dashboards for advisors and tutors.
Conference Paper
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The growing participation of educational institutions in the movement of massive open online courses (MOOCs) generates a great multitude of opportunities and challenges. Firstly, the high dropout rate and the lack of learners' motivation are significant issues in MOOCs, which cast doubt on the quality of teaching and learning. Secondly, MOOC users' diversity creates problems and barriers to MOOC providers in designing successful and effective courses that will suit all types of users. It is still unclear to think about how to reach various types of MOOC participants. In this regard, gamification is proposed as a complement to existing learning approaches to provide learners with a powerful and motivational learning experience. For decreasing the high dropout rates in MOOCs and to engage the learners and improve their motivation by adopting gamification in education, this study aims to classify gamification into three categories: achievement, social, and immersion (ASI). Furthermore, 27 gaming elements are identified that have been used in a MOOC context and are mapped to the aforementioned categories in order to help the MOOC developers and designers better understand how the various gamification categories can influence the users.
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In online learning, the dropout phenomenon is a relevant issue to address with practical solutions. Several data sets stimulate original, and resolutive data analysis approaches, demonstrating the importance of the dropout phenomenon. This study proposes a novel approach to predicting massive online open course (MOOC) students at risk of dropout stressing the need to consider the temporal dimension in the data log. The proposal aims to build a data‐driven decision support system able to identify students at risk of dropout based on the conceptualization of such students' behavior and its evolution along the time dimension. The primary theoretical model behind the proposed method is the formal concept analysis, and its temporal extension (i.e., temporal concept analysis) for analyzing timestamped data and carrying out a timed lattice. The main result of the paper is a method to extract behavioral patterns of MOOC students at risk of dropout. Such patterns are defined as Time‐based Behavior Rules extracted from the aforementioned timed lattice obtained through the preprocessing of MOOC platform log files. The resulting rule set can be easily integrated for implementing educational DSS, as shown in the last part of the paper. The conducted experiments reveal promising results in terms of F‐score and students' monitoring time.
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The implementation of electronic team training, e-team training, has been used to teach teamwork skills in a wide array of industries. By using e-team training, organizations have seen the observable benefits of improved team effectiveness, faster response times, and reduction in training costs. Those learning from e-team training have reported additional benefits of improved communication skills, learning team leadership tactics, and an improved satisfaction in their training over traditional team training methods. MOOCs, massive open online course, have yet to incorporate e-team training, due to previous technologies having unscalable, limiting constraints. We reviewed literature on how utilizing e-team training in virtual worlds improves current computer-supported collaborative learning ( CSCL) available in MOOCs, examined the constraints, and give recommendations for the best practices of moving e-team training in virtual worlds to at-scale.
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This study aims to explore and improve ways of handling a continuous variable dataset, in order to predict student dropout in MOOCs, by implementing various models, including the ones most successful across various domains, such as recurrent neural network (RNN), and tree-based algorithms. Unlike existing studies, we arguably fairly compare each algorithm with the dataset that it can perform best with, thus ‘like for like’. I.e., we use a time-series dataset ‘as is’ with algorithms suited for time-series, as well as a conversion of the time-series into a discrete-variables dataset, through feature engineering, with algorithms handling well discrete variables. We show that these much lighter discrete models outperform the time-series models. Our work additionally shows the importance of handing the uncertainty in the data, via these ‘compressed’ models.
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Goal setting is an important component in successful teaching and learning, but relatively little is known about its impact on course persistence and achievement in massive open online courses. Using an experimental design and employing a variety of data including student writings, content‐related assignment attempts, and quantitative achievement in the courses, we compared the outcomes of two groups of learners who were given different writing prompts at the beginning of their course. While no overall effects of writing prompt type on the dependent variables were observed, highly statistically significant differences were found when goal writings were more closely examined and compared via qualitative coding. When learners’ written responses to prompts contained either learning or performance goals, those participants both achieved more and engaged in learning longer than participants whose written responses did not fall into either of these categories. Practitioner notes What is already known about this topic Goals are related to students’ behaviors and performance. Performance goals’ influences on learning have inconsistent results, while learning goals are considered beneficial. What this paper adds The effects of conscious goal setting in massive open online courses (MOOCs) may be different from traditional learning contexts. Having either learning and performance goals results in better persistence and performance than not having these goals. Implications for practice and/or policy More interventions should be designed to help MOOC learners set and commit to their goals. Use MOOC learner's learning and performance goals to promote learning and persistence. What is already known about this topic Goals are related to students’ behaviors and performance. Performance goals’ influences on learning have inconsistent results, while learning goals are considered beneficial. What this paper adds The effects of conscious goal setting in massive open online courses (MOOCs) may be different from traditional learning contexts. Having either learning and performance goals results in better persistence and performance than not having these goals. Implications for practice and/or policy More interventions should be designed to help MOOC learners set and commit to their goals. Use MOOC learner's learning and performance goals to promote learning and persistence.
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Massive open online courses, MOOCs, are a recent phenomenon that has achieved a tremendous media attention in the online education world. Certainly, the MOOCs have brought interest among the learners (given the number of enrolled learners in these courses). Nevertheless, the rate of dropout in MOOCs is very important. Indeed, a limited number of the enrolled learners complete their courses. The high dropout rate in MOOCs is perceived by the educator’s community as one of the most important problems. It’s related to diverse aspects, such as the motivation of the learners, their expectations and the lack of social interactions. However, to solve this problem, it is necessary to predict the likelihood of dropout in order to propose an appropriate intervention for learners at-risk of dropping out their courses. In this paper, we present a dropout predictor model based on a neural network algorithm and sentiment analysis feature that used the clickstream log and forum post data. Our model achieved an average AUC (Area under the curve) as high as 90% and the model with the feature of the learner’s sentiments analysis attained average increase in AUC of 0.5%.
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One of the problems regarding MOOCs (Massive Open Online Courses) is the high dropout rate. Although dropout periods have been studied, there is still a lack of understanding of how dropout differs for MOOCs with different levels of difficulty. A quantitative study was conducted to determine the periods with the highest dropouts in computer programming MOOCs and the performance of the dropouts on the course before dropping out. Four occurrences of three MOOCs, with different durations, difficulty of the topic, and the degree of supportive methods, were included. The results showed that dropout was highest at the beginning of all studied courses. Learners also dropped out before the project. In the easier and shorter courses, most dropouts were successful until they quit the course. In longer and more difficult courses, learners mainly dropped out in the week they started due to experiencing problems with the course activities. It is suggested to recommend that learners take courses at a level that suits them if their current course is too easy or difficult and encourage learners to use course resources for help. It would be a good idea to provide learners with example topics to assist them in starting with a project.
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This research examines the crises of attrition in the students’ population and study programs using descriptive statistics interpretation for solving social isolation for traditional face-to-face classroom education. The study used a descriptive research design with ‘variable values’ to examine two-degree programs. The study used several testing methods to evaluate the statistical analysis of the social and academic characteristics of freshmen students in both the Informatics and Computer Science programs at the University of South Carolina Upstate from Fall 2018 to Fall 2019. The criterion variable was the student outcome (persistence or dropout), while the general structure matrix pattern was examined to validate the convergent factors. The methodology included a variance of the eigenfunction and values for interpreting the factor structure of the variable values. The findings suggest several mitigating factors which include improved persistence of “enrollment number, program delivery mode, GPA at time of completion and dropout, student orientation, and courses completed at the time of student dropout would help improve academic success for students.
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Neuromatch Academy (NMA) designed and ran a fully online 3-week Computational Neuroscience Summer School for 1757 students with 191 teaching assistants (TAs) working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility.
Chapter
With the development and application of online education, mining friend relationships between learners can improve interactive communication, enhance collaborative learning, and motivate learners to make mutual progress. However, existing methods only recommend well-known members through the number of likes and fans, which fail to consider the hidden interest points and content topics of members in the text. To address this problem, we propose an Evaluation Latent Dirichlet Allocation (EvaluationLDA) algorithm to recommend suitable friends for learners in online education. The EvaluationLDA algorithm clusters learners with similar learning interests to obtain Top-N friend recommendation sequences based on constructing learner document datasets, calculating learner similarity, and modeling the friend topic. We conduct experiments to demonstrate the effectiveness of our EvaluationLDA algorithm. The result shows that our EvaluationLDA algorithm can effectively recommend Top-N friend sequences in the online education platform.
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Massive Open Online Courses (MOOCs) are flexible offerings that deliver content to a large audience in a virtual platform. MOOCs are increasingly accessed by health professionals to support their own professional development. Despite the agreed usefulness of MOOCs, the rates of adoption are still extremely low. This study sought to understand the personal and social factors associated with MOOC adoption. Participants were newly graduated occupational therapists who registered for a leadership skills development MOOC. Qualitative interviews were conducted to understand unique perspectives of participants who did and did not complete the MOOC. Data were analyzed using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Participants reported they found the MOOC content beneficial in providing a foundational framework on which to develop their leadership skills. Even though MOOC content was organized into multiple small components, participants shared that they would engage with the material once a week for up to two hours. Participants reported a high level of comfort accessing the technology to complete the MOOC, however they reported that they would have preferred more interactive or synchronous learning opportunities. MOOCs are an efficient way to offer a wide variety of educational offerings to health professionals. Despite their asynchronous nature, MOOC developers should consider maximizing opportunities for learner interaction and content application learning opportunities within MOOCs to increase their overall adoption.
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Η ικανότητα αυτο-ρύθμισης της μάθησης αποτελεί για τους εκπαιδευόμενους μια κρίσιμη δεξιότητα όταν συμμετέχουν σε εκπαιδευτικά περιβάλλοντα στα οποία η υποστήριξη και η καθοδήγηση του εκπαιδευτή είναι ελάχιστη ή απουσιάζει εντελώς. Ένα τέτοιο περιβάλλον είναι και τα Μαζικά Ανοικτά Διαδικτυακά Μαθήματα (MOOCs). Μια αυτο-ρυθμιστική στρατηγική που φαίνεται να βοηθά προς την κατεύθυνση της επίτευξης των στόχων των εκπαιδευομένων, μέσω της αυτο-ρύθμισής τους, είναι η Ψυχική αντίθεση με προθέσεις υλοποίησης (Mental Contrasting With Implementation Intentions - MCII) η οποία συνδυάζει μεταξύ τους δύο αυτορυθμιστικές τεχνικές, την Ψυχική αντίθεση (MC) με την Πρόθεση υλοποίησης (ΙΙ). Στην παρούσα εργασία, θα γίνει παρουσίαση του ηλεκτρονικού εργαλείου που αναπτύχθηκε στο πλαίσιο της διδακτορικής έρευνας του πρώτου ερευνητή, το οποίο υλοποιεί τη στρατηγική MCII και την επεκτείνει, ενσωματώνοντας χαρακτηριστικά που υποστηρίζουν διάφορες αυτορυθμιστικές διεργασίες του μοντέλου αυτο-ρύθμισης του Zimmerman.
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Η αυτορρύθμιση μπορεί να θεωρηθεί μια διαδικασία που βοηθά τα άτομα να ξεπεράσουν εμπόδια που εμφανίζονται στην προσπάθειά τους να επιτύχουν τα επιθυμητά, γι’ αυτούς, αποτελέσματα και αποτελεί μια κρίσιμη ικανότητα σε περιβάλλοντα εξΑΕ, όπως είναι τα MOOCs. Από την άλλη, οι αυτορυθμιστικές στρατηγικές είναι τα εργαλεία που τους βοηθούν να μετατρέψουν τα κίνητρα και τις προσδοκίες επιτυχίας που έχουν, σε κατάλληλες δράσεις προς αυτήν την κατεύθυνση. Μια τέτοια στρατηγική είναι η στρατηγική της Ψυχικής αντίθεσης με προθέσεις υλοποίησης (Mental Contrasting With Implementation Intentions - MCII). Στην παρούσα εργασία θα παρουσιαστούν τα αποτελέσματα της αυτορρύθμισης των συμμετεχόντων στο πρώτο πρόγραμμα MOOC του Πανεπιστημίου Αιγαίου, οι οποίοι εφάρμοσαν την συγκεκριμένη αυτορρυθμιστική στρατηγική σε συνδυασμό με διεργασίες του αυτορρυθμιστικού μοντέλου του Zimmerman.
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MOOCs have attracted hundreds of millions of learners with advantages such as being cost-free and having flexible time and space. However, high dropout rates have become the main issue that hinders their further progress. To solve this problem, this research proposes a pipeline model named CLSA to predict the dropout rate based on learners’ behavior data. The CLSA model first uses a convolutional neural network to extract local features and builds feature relations using a kernel strategy. Then, it feeds this high-dimensional vector generated by the CNN to a long short-term memory network to obtain a time-series incorporated vector representation. After that, it employs a static attention mechanism on the vector to obtain an attention weight on each dimension. When tested on the KDD CUP 2015 dataset, our model reached 87.6% accuracy, which was higher than the previous best model (over 2.8%). The F1-score of our model reached 86.9%, which was 1.6% higher than the previous state-of-the-art result.
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Ένας από τους λόγους δημιουργίας των MOOCs ήταν για να προσφέρουν δυνατότητες μόρφωσης σε άτομα που είναι αποκλεισμένα από την τριτοβάθμια εκπαίδευση για οι-κονομικούς ή άλλους λόγους. Η παρούσα εργασία, η οποία αποτελεί τμήμα της βιβλιο-γραφικής ανασκόπησης ερευνών στο πλαίσιο της διδακτορικής έρευνας του πρώτου ερευνητή, επιχειρεί να σκιαγραφήσει τα δημογραφικά χαρακτηριστικά των ατόμων που εγγράφονται σε μαθήματα MOOCs, καθώς επίσης να καταγράψει τα κίνητρα και τους στόχους που τους ωθούν στο να πάρουν αυτή την απόφαση και τα αρχικά συναισθή-ματα που βιώνουν πριν από την έναρξη των μαθημάτων. Τα ευρήματα μπορούν να ληφθούν υπόψη κατά τον εκπαιδευτικό σχεδιασμό Μαζικών Ανοικτών Διαδικτυακών Μαθημάτων (MOOC).
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Tinto's [Rev. Educ. Res. 45 (1975) 89; Tinto, V. (1987). Leaving college. Chicago: The University of Chicago Press; Tinto, V. (1993). Leaving college: rethinking the causes and cures of student attrition. Chicago: The University of Chicago Press] student integration model and Bean and Metzner's [Rev. Educ. Res. 55 (1985) 485] student attrition model have been influential in explaining persistence and attrition in higher education programs. However, these models were developed with on-campus programs in mind and, although they are broadly relevant to distance education programs, their ability to explain the persistence of online students is limited. Distance education students have characteristics and needs that differ from traditional learners and the virtual learning environment differs in important ways from an on-campus environment. This article draws chiefly from Tinto's and Bean and Metzner's models and the results of research into the needs of online distance education students in order to synthesize a composite model to better explain persistence and attrition among the largely nontraditional students that enroll in online courses.
MOOCs are really a platform
  • G Siemens
Siemens, G.: MOOCs are really a platform. Elearnspace (2012), http://www.elearnspace.org/blog/2012/07/25/moocs-are-reallya-platform/ (last edited July 25, 2012) (accessed 15 February 2013)
Knowledge Management in Organizations
  • R Hernández Rizzardini
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Hernández Rizzardini, R., Gütl, C., Chang, V., Morales, M.: MOOC in Latin America: Implementation and Lessons Learned. In: 2nd International Workshop on Learning Technology for Education in Cloud (LTEC), Knowledge Management in Organizations, pp. 147-158. Springer Netherlands (2013)
Understanding MOOCs as an Emerging Online Learning Tool: Perspectives from the Students
  • M Liu
  • J Kang
  • M Cao
  • M Lim
  • Y Ko
  • A Schmitz Weiss
Liu, M., Kang, J., Cao, M., Lim, M., Ko, Y., Schmitz Weiss, A.: Understanding MOOCs as an Emerging Online Learning Tool: Perspectives from the Students. In: Proceedings of E-Learn (2013)