What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach

Hew, Khe Foon; Hu, Xiang; Qiao, Chen; Tang, Ying

doi:10.1016/j.compedu.2019.103724

Cited by 231 publications

(149 citation statements)

References 65 publications

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“…Terbukti dari hasil penelitian hanya 4 orang informan yang merasa sangat puas dengan PJJ yang berlangsung saat ini. Sementara itu, faktor lainnya yang berpengaruh pada kepuasan mahasiswa menjalani PJJ adalah kelas dosen, konten, penilaian dan jadwal (Hew, Hu, Qiao, & Tang, 2020). Selain konten, pendekatan pembelajaran yang terintegrasi dengan bahasa juga memengaruhi kepuasan mahasiswa dalam mengikuti PJJ (Granel, Leyva-Moral, Bernabeu-Tamayo, Gómez-Ibáñez, Watson, & Aguayo-González, 2019).…”

Section: Gambar 10 Tingkat Kepuasan Terhadap Pjjunclassified

Dampak pandemi Covid-19 terhadap kepuasan pembelajaran jarak jauh

Napitupulu

2020

jitp

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Pandemi Covid-19 saat ini berdampak pada perguruan tinggi. IAIN Padangsidimpuan sebagai salah satu institusi pendidikan tinggi negeri keagamaan Islam di Indonesia dituntut untuk mengikuti perubahan metode pembelajaran yaitu pembelajaran jarak jauh (PJJ) yang semula sepenuhnya dilakukan dengan tatap muka. Letak kampus yang berada di Bagian Selatan Sumatera Utara dengan asal mahasiswa yang beragam dan berada jauh dari perkotaan menjadi tantangan tersendiri bagi institusi. Penelitian ini menggunakan pendekatan kualitatif dengan analisis deskriptif. Jumlah informan 384 orang yang terdiri dari mahasiswa aktif Fakultas Ekonomi dan Bisnis Islam IAIN Padangsidimpuan yang dipilih secara acak. Berdasarkan hasil penelitian diketahui bahwa, meskipun mayoritas mahasiswa (95,8%) sudah memiliki perangkat untuk menjalani PJJ, namun di sisi lain mahasiswa merasa metode PJJ saat ini belum tepat karena mahasiswa merasa tidak dapat memantau perkembangan PJJ dengan mudah, tidak dapat memperoleh materi pembelajaran dengan mudah juga tidak dapat mempelajari materi dengan mudah. Secara keseluruhan, baik dari sisi teknologi maupun sisi dosen, mahasiswa tidak puas dengan metode PJJ yang dijalaninya saat ini dan juga merasa tidak puas dengan kemampuan dosen dalam menyampaikan materi pada PJJ. AbstractThe Covid19 pandemic is currently affecting universities. IAIN Padangsidimpuan as one of the institutions of Islamic religious state higher education in Indonesia is required to follow the changes in learning methods, namely distance learning (PJJ), which is from the beginning is fully face-to-face. The location of the campus in the Southern Part of North Sumatra with diverse origins of students and being far from urban areas is a challenge for institutions. This research uses a qualitative approach with descriptive analysis. The number of informants 384 people consisting of active students of the Faculty of Islamic Economics and Business were randomly selected. Based on the results of the study note that, although the majority of students (95.8%) already have the tools to undergo Distance Learning, on the other hand students feel the distance learning method is currently not appropriate because students feel unable to monitor the development of distance learning easily, cannot obtain learning material easily nor can study material easily. Overall, both in terms of technology and the Lecturer side, students are not satisfied with the distance learning method they are currently undergoing and also feel dissatisfied with the ability of the Lecturer to deliver material to distance learning.

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Section: Gambar 10 Tingkat Kepuasan Terhadap Pjjunclassified

Dampak pandemi Covid-19 terhadap kepuasan pembelajaran jarak jauh

Napitupulu

2020

jitp

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“…To estimate the probability of new university students failing throughout their learning process, AI applications can be employed as such (Wu, Chen, & Tsai, 2018). There have been several attempts (e.g., Abu Zohair, 2019;Hew, Hu, Qiao, & Tang, 2020;Pérez, Castellanos, & Correal, 2018) to predict student performance or dropout using algorithms in higher education research in order to help at-risk students by assuring their retention. For instance, to predict students' performance in a university course, Abu Zohair (2019) used clustering algorithms and a small dataset for training and model construction, establishing a reliable and accurate prediction model with a prediction accuracy of approximately 70%.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, to predict students' performance in a university course, Abu Zohair (2019) used clustering algorithms and a small dataset for training and model construction, establishing a reliable and accurate prediction model with a prediction accuracy of approximately 70%. Hew et al (2020) adopted the supervised machine learning algorithm and hierarchical linear modelling to analyze the features of massive open online courses (MOOCs) and students' perceptions of MOOCs; they found that several course features such as instructor, content, assessment, and schedule significantly predict student satisfaction. In a recent systematic review (Zawacki-Richter, Marín, Bond, & Gouverneur, 2019) of research on AI applications in higher education, the paper found that studies pertaining to dropout and retention intended to develop early warning systems to detect at-risk students in their first year.…”

Section: Introductionmentioning

confidence: 99%

Precision education with statistical learning and deep learning: a case study in Taiwan

Tsai

Chen

Shiao

et al. 2020

Int J Educ Technol High Educ

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The low birth rate in Taiwan has led to a severe challenge for many universities to enroll a sufficient number of students. Consequently, a large number of students have been admitted to universities regardless of whether they have an aptitude for academic studies. Early diagnosis of students with a high dropout risk enables interventions to be provided early on, which can help these students to complete their studies, graduate, and enhance their future competitiveness in the workplace. Effective prelearning interventions are necessary, therefore students' learning backgrounds should be thoroughly examined. This study investigated how big data and artificial intelligence can be used to help universities to more precisely understand student backgrounds, according to which corresponding interventions can be provided. For this study, 3552 students from a university in Taiwan were sampled. A statistical learning method and a machine learning method based on deep neural networks were used to predict their probability of dropping out. The results revealed that student academic performance (regarding the dynamics of class ranking percentage), student loan applications, the number of absences from school, and the number of alerted subjects successfully predicted whether or not students would drop out of university with an accuracy rate of 68% when the statistical learning method was employed, and 77% for the deep learning method, in the case of giving first priority to the high sensitivity in predicting dropouts. However, when the specificity metric was preferred, then the two approaches both reached more than 80% accuracy rates. These results may enable the university to provide interventions to students for assisting course selection and enhancing their competencies based on their aptitudes, potentially reducing the dropout rate and facilitating adaptive learning, thereby achieving a win-win situation for both the university and the students. This research offers a feasible direction for using artificial intelligence applications on the basis of a university's institutional research database.

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“…Response-ability refers to one's ethical sensitivity and the ability to respond accordingly. Haraway (2016) defines it as 'cultivating collective knowing and doing' (p. 34), 'sympoiesis' (making-with) (p. 58), and, as responses of becoming-with and rendering each other capable [italics added for emphasis]". Deterritorialization, borrowed from Deleuze and Guattari (1987), entails establishing a new relationship, new process, and differentiation of role.…”

Section: Resultsmentioning

confidence: 99%

Undergoing the experiences of the COVID-19 pandemic as ruptures in American civil society create conditions for right action

Nicolaides

Lim

2020

Reflective Practice

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The global pandemic known as COVID-19 has brought most cities and counties to a slow dance of shelter-in-place and stay-at-home curfews. This is our first personally experienced civilization-wide transformation. Nothing will return to how we knew it. The complexity of this moment-the full vitality of the unknown-is an essential feature of transformation and demands an ongoing process of moving underneath the experience of the unknown to generate right action. In this article, we will write from our own experience of COVID-19 and the rupture of civil unrest in the wake of the murder of George Floyd, Breonna Taylor, and Ahmaud Arbery. We believe that the global pandemic, and the potential of this moment in America to overcome and defeat racial disparity, suggests a space for the forging of deeper stories-stories that peel back the felt sense and fullness of a present unfolding experience. We will discuss living through these experiences as the basis for exploring the potential for the transformation of sense-making that may generate a multiplicity of new learning and new action.

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What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach

Cited by 231 publications

References 65 publications

Dampak pandemi Covid-19 terhadap kepuasan pembelajaran jarak jauh

Dampak pandemi Covid-19 terhadap kepuasan pembelajaran jarak jauh

Precision education with statistical learning and deep learning: a case study in Taiwan

Undergoing the experiences of the COVID-19 pandemic as ruptures in American civil society create conditions for right action

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