scholarly journals Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model

2021 ◽  
Vol 336 ◽  
pp. 05008
Author(s):  
Cheng Wang ◽  
Sirui Huang ◽  
Ya Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Massive Open Online ◽  
Data Set ◽  
Proposed Model ◽  
Contextual Feature

The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.

scholarly journals A Bayesian CNN-LSTM Model for Sentiment Analysis in Massive Open Online Courses MOOCs

2021 ◽  
Vol 16 (23) ◽  
pp. 216-232
Author(s):  
Khaoula Mrhar ◽  
Lamia Benhiba ◽  
Samir Bourekkache ◽  
Mounia Abik
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
Dropping Out ◽  
Bayesian Neural Network ◽  
Massive Open Online ◽  
Collaborative Tools ◽  
Novel Approach ◽  
Analysis Task

Massive Open Online Courses (MOOCs) are increasingly used by learn-ers to acquire knowledge and develop new skills. MOOCs provide a trove of data that can be leveraged to better assist learners, including behavioral data from built-in collaborative tools such as discussion boards and course wikis. Data tracing social interactions among learners are especially inter-esting as their analyses help improve MOOCs’ effectiveness. We particular-ly perform sentiment analysis on such data to predict learners at risk of dropping out, measure the success of the MOOC, and personalize the MOOC according to a learner’s behavior and detected emotions. In this pa-per, we propose a novel approach to sentiment analysis that combines the advantages of the deep learning architectures CNN and LSTM. To avoid highly uncertain predictions, we utilize a Bayesian neural network (BNN) model to quantify uncertainty within the sentiment analysis task. Our em-pirical results indicate that: 1) The Bayesian CNN-LSTM model provides interesting performance compared to other models (CNN-LSTM, CNN, LSTM) in terms of accuracy, precision, recall, and F1-Score; and 2) there is a high correlation between the sentiment in forum posts and the dropout rate in MOOCs.

scholarly journals Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245485
Author(s):  
Asra Khalid ◽  
Karsten Lundqvist ◽  
Anne Yates ◽  
Mustansar Ali Ghzanfar
Keyword(s):  
Recommender Systems ◽  
Information Overload ◽  
Online Courses ◽  
Dynamic Environment ◽  
Poor Quality ◽  
Massive Open Online Courses ◽  
Learning Resources ◽  
Massive Open Online ◽  
Data Set ◽  
Online Learning Resources

Massive Open Online Courses (MOOCs) have gained in popularity over the last few years. The space of online learning resources has been increasing exponentially and has created a problem of information overload. To overcome this problem, recommender systems that can recommend learning resources to users according to their interests have been proposed. MOOCs contain a huge amount of data with the quantity of data increasing as new learners register. Traditional recommendation techniques suffer from scalability, sparsity and cold start problems resulting in poor quality recommendations. Furthermore, they cannot accommodate the incremental update of the model with the arrival of new data making them unsuitable for MOOCs dynamic environment. From this line of research, we propose a novel online recommender system, namely NoR-MOOCs, that is accurate, scales well with the data and moreover overcomes previously recorded problems with recommender systems. Through extensive experiments conducted over the COCO data-set, we have shown empirically that NoR-MOOCs significantly outperforms traditional KMeans and Collaborative Filtering algorithms in terms of predictive and classification accuracy metrics.

scholarly journals Hierarchical Reinforcement Learning for Course Recommendation in MOOCs

2019 ◽  
Vol 33 ◽  
pp. 435-442 ◽  
Author(s):  
Jing Zhang ◽  
Bowen Hao ◽  
Bo Chen ◽  
Cuiping Li ◽  
Hong Chen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Online Courses ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Massive Open Online Courses ◽  
User Profiles ◽  
Massive Open Online ◽  
Hierarchical Reinforcement Learning ◽  
Proposed Model ◽  
Recent Attention

The proliferation of massive open online courses (MOOCs) demands an effective way of personalized course recommendation. The recent attention-based recommendation models can distinguish the effects of different historical courses when recommending different target courses. However, when a user has interests in many different courses, the attention mechanism will perform poorly as the effects of the contributing courses are diluted by diverse historical courses. To address such a challenge, we propose a hierarchical reinforcement learning algorithm to revise the user profiles and tune the course recommendation model on the revised profiles.Systematically, we evaluate the proposed model on a real dataset consisting of 1,302 courses, 82,535 users and 458,454 user enrolled behaviors, which were collected from XuetangX—one of the largest MOOCs in China. Experimental results show that the proposed model significantly outperforms the state-of-the-art recommendation models (improving 5.02% to 18.95% in terms of HR@10).

scholarly journals Τα MOOCs ως Πολύπλοκο Σύστημα: Πρόταση Ενός Υβριδικού Μοντέλου Στηριζόμενο στην Αρχή της Διαθεματικότητας / ΜΟΟCs as a Complex System: Α proposal of a Hybrid Model Based on the Principle of Interdisciplinarity

2020 ◽  
Vol 16 (1) ◽  
pp. 64
Author(s):  
Θεοδώρα Κουβαρά ◽  
Χριστόφορος Καραχρήστος ◽  
Θεοφάνης Ορφανουδάκης ◽  
Ηλίας Σταυρόπουλος
Keyword(s):  
Complex System ◽  
Hybrid Model ◽  
Online Courses ◽  
Current Model ◽  
Massive Open Online Courses ◽  
Assessment Process ◽  
Alternative View ◽  
Massive Open Online ◽  
Pedagogical Design ◽  
Proposed Model

Τα πρώτα Μαζικά Ανοικτά Διαδικτυακά Μαθήματα (Massive Open Online Courses -  MOOCs) ήταν εδραιωμένα στη θεωρία του Κονεκτιβισμού. Στόχος ήταν η μάθηση να παρέχεται μέσα από μια διαδικασία αλλεπάλληλης δικτύωσης και αλληλεπίδρασης των συμμετεχόντων σε διαφορετικά σημεία στο διαδίκτυο. Ωστόσο, αν και ο παιδαγωγικός σχεδιασμός των πρώτων MOOCs κρίνεται έως σήμερα από πολλούς ερευνητές καταλληλότερος, στην πορεία, παρατηρήθηκε στροφή προς την υιοθέτηση MOOCs βάσει πιο παραδοσιακών μεθόδων. Ο λόγος είναι πως η ανοιχτότερη και πιο ελεύθερη φύση των πρώτων MOOCs δημιουργούσε εμπόδια στην εφαρμογή της αξιολόγησης. Σκοπός της παρούσας μελέτης είναι μια ανασκόπηση των υβριδικών μοντέλων MOOC και η εισήγηση ενός νέου υβριδικού μοντέλου MOOC το οποίο θα συνδυάζει την υψηλή ποιότητα αξιολόγησης με τις βασικές παιδαγωγικές ιδιότητες των πρώτων MOOCs. Πρόκειται για μία εναλλακτική θεώρηση του Κονεκτιβισμού, εδραιωμένη στην Θεωρία του Χάους από την οποία και γεννήθηκε το συγκεκριμένο ρεύμα. Το προτεινόμενο μοντέλο προβάλλει το MOOC ως πολύπλοκο σύστημα, εισάγοντας τη διαθεματικότητα ως στοιχείο που προωθεί τη διάδραση, την αυτονομία, τη διασύνδεση και την ανοιχτότητα, συμβάλλοντας τοιουτοτρόπως στην ανάδυση ιδεών και στην παραγωγή της γνώσης μέσα από μη γραμμικές ακολουθίες. Στόχος είναι να χαράξει πολλά και διαφορετικά μονοπάτια μάθησης, παρέχοντας τη δυνατότητα στον κάθε συμμετέχοντα να πραγματοποιεί και να θέτει διαφορετικούς στόχους και κίνητρα, χωρίς να υποβαθμίζεται η διαδικασία της αξιολόγησης.The first Massive Open Online Courses (MOOCs) were premised upon the theory of Connectivism. The aim was to provide learning through a process of successive networking and interaction of participants at different points on the internet. However, although the pedagogical design of the first MOOCs is still considered by many researchers to be more appropriate, over time, there has been a shift towards adopting MOOCs based on more traditional methods. The reason is that the more open and free nature of the first MOOCs created obstacles in the implementation of the evaluation. The purpose of this study is to review the MOOC hybrid models and to propose a new MOOC hybrid model that will combine the high quality of evaluation with the basic pedagogical properties of the first MOOCs. It is an alternative view of Connectivism, based on Chaos Theory from which this current model was born. The proposed model promotes MOOC as a complex system, introducing interdisciplinarity as an element that promotes interaction, autonomy, interconnection, and openness, thus contributing to the emergence of ideas and the production of knowledge through non-linear sequences. The goal is to chart many different learning paths, enabling each participant to achieve and set different goals and motives without degrading the assessment process.

Education and the Relation to the Outside: A Little Real Reality

2015 ◽  
Vol 9 (3) ◽  
pp. 273-300 ◽  
Author(s):  
David Savat ◽  
Greg Thompson
Keyword(s):  
Performance Metrics ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Educational Institutions ◽  
Special Issue ◽  
Massive Open Online ◽  
Transformative Teaching ◽  
Control Society ◽  
Brave New World ◽  
Radical Transformation

One of the more dominant themes around the use of Deleuze and Guattari's work, including in this special issue, is a focus on the radical transformation that educational institutions are undergoing, and which applies to administrator, student and educator alike. This is a transformation that finds its expression through teaching analytics, transformative teaching, massive open online courses (MOOCs) and updateable performance metrics alike. These techniques and practices, as an expression of control society, constitute the new sorts of machines that frame and inhabit our educational institutions. As Deleuze and Guattari's work posits, on some level these are precisely the machines that many people in their day-to-day work as educators, students and administrators assemble and maintain, that is, desire. The meta-model of schizoanalysis is ideally placed to analyse this profound shift that is occurring in society, felt closely in the so-called knowledge sector where a brave new world of continuous education and motivation is instituting itself.

cMOOCs and Global Learning: An Authentic Alternative

2013 ◽  
Vol 17 (2) ◽  
Author(s):  
Carol Yeager ◽  
Betty Hurley-Dasgupta ◽  
Catherine A. Bliss
Keyword(s):  
Online Courses ◽  
Lessons Learned ◽  
Massive Open Online Courses ◽  
Research Review ◽  
Press Coverage ◽  
Open Educational Resource ◽  
Educational Resource ◽  
Global Learning ◽  
Massive Open Online ◽  
Definition Of

Massive Open Online Courses (MOOCs) continue to attract press coverage as they change almost daily in their format, number of registrations and potential for credentialing. An enticing aspect of the MOOC is its global reach. In this paper, we will focus on a type of MOOC called a cMOOC, because it is based on the theory of connectivism and fits the definition of an Open Educational Resource (OER) identified for this special edition of JALN. We begin with a definition of the cMOOC and a discussion of the connectivism on which it is based. Definitions and a research review are followed with a description of two MOOCs offered by two of the authors. Research on one of these MOOCs completed by a third author is presented as well. Student comments that demonstrate the intercultural connections are shared. We end with reflections, lessons learned and recommendations.

Massive Open Online Courses: Potential Implications for Advancing the Field of Family Science

2016 ◽  
Vol 21 (3) ◽  
Author(s):  
Karen Doneker ◽  
Bethany Willis Hepp ◽  
Debra Berke ◽  
Barbara Settles
Keyword(s):  
Online Courses ◽  
Massive Open Online Courses ◽  
Massive Open Online ◽  
Family Science

Valor dos massive open online course (MOOC) na educação para a cidadania

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Hermano Carmo ◽  
Teresa Maia e Carmo
Keyword(s):  
Online Courses ◽  
Massive Open Online Courses ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Tecnología Educativa

A sociedade contemporânea é marcada por três macrotendências que a identificam como uma sociedade singular na história humana: processo de mudança acelerada, desigualdade crescente e fibrilhação dos sistemas de poder. Tais tendências têm tido como efeitos um quadro de ameaças e oportunidades que tanto têm constituído gigantesco desafio aos sistemas educativos quanto configuram a urgência de ressocialização de todas as gerações vivas no sentido da construção de uma cidadania global. Nesse contexto, propõe-se um modelo que configura uma estratégia de educação para a cidadania, com dois eixos, quatro vertentes e dez áreas-chave. Seguidamente, descreve-se e discute-se a emergência quase explosiva dos Massive Open Online Courses (MOOC) a partir de instituições de ensino superior internacionalmente reconhecidas, no quadro do novo paradigma digital, sua diversidade e seu potencial ainda em aberto. Confrontando a nova abordagem educativa com o modelo de educação para a cidadania proposto, conclui-se constituir um meio robusto para o potenciar.Palavras-chave:Conjuntura. Macrotendências. Educação para a cidadania. MOOC. Tecnologia educativa. Paradigma digital.Link: http://revista.ibict.br/inclusao/article/view/4171/3642

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