An empirical analysis of video viewing behaviors in flipped CS1 courses

Dazo, Suzanne L.; Stepanek, Nicholas R.; Fulkerson, Robert; Dorn, Brian

doi:10.1145/3007625

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…Cara seperti ini kurang efektif karena peserta didik harus membuka dan menutup jendela aplikasi. Selain itu, kekurangan dari pembelajaran menggunakan video adalah hilangnya sisi interaktivitas dari multimedia pembelajaran yang berpotensi mengakibatkan peserta didik belajar secara pasif menonton tanpa diikuti proses pembelajaran mandiri dan menonton ulang biasanya sangat jarang terjadi [1].…”

Section: Pendahuluanunclassified

Penerimaan Pembelajar terhadap Penggunaan Scrimba sebagai Multimedia Pembelajaran Interaktif

Wulan

Setiarini

2021

Edsence

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Dalam mempelajari bahasa pemrograman atau framework bahasa pemrograman tertentu yang digunakan untuk pengembangan web, mahasiswa dapat mempelajarinya secara mandiri dengan cara menonton video pembelajaran, membaca dokumentasi, membaca buku mengenai hal tersebut. Namun, cara tersebut lebih cenderung satu arah, dimana, pembelajar tidak dapat mencoba langsung contoh kasus dalam media-media tersebut. Mereka harus membuka editor, membuka browser, menyalakan server lokal, menulis ulang contoh, dan menjalankan program. Perpindahan jendela aplikasi merepotkan pembelajar. Scrimba merupakan sebuah inovasi multimedia pembelajaran di mana pengguna dapat menonton video pembelajaran sekaligus berinteraksi langsung pada kode program, dan menjalankan program saat video dihentikan. Editor program, video pembelajaran, dan browser untuk menguji program ada pada satu layar. Penelitian ini bertujuan untuk mengetahui penerimaan pembelajar terhadap Scrimba, bagaimana interaksi mereka dan bagaimana kepuasan mereka. Metode penelitian yang digunakan adalah metode deskriktif dengan pendekatan survei. Penelitian dilakukan terhadap mahasiswa berprogram studi Informatika yang telah mengeksplorasi sebuah materi pembelajaran yang dirasakan sebagian besar oleh mereka sulit. Dosen merekam tutorial pembelajaran di Scrimba yang dibagikan kepada mahasiswa untuk dipelajari. Hasil penelitian menemukan bahwa Scrimba berkontribusi positif terhadap pemahaman materi pembelajaran dan kepuasan mahasiswa dalam mempelajari materi di Scrimba. Rata-rata hasilnya didominasi pada level cukup baik yakni sebesar 42,55% ke baik dan sangat baik yang jika dijumlahkan sebesar 49,03%. Sehingga, dapat disimpulkan bahwa Scrimba dapat menjadi salah satu alternatif alat pembelajaran untuk mempelajari pengembangan web yang layak digunakan untuk pembelajaran jarak jauh. Bahkan, pendekatan rekaman layar dan audio dan kode editor dalam satu layar seperti di Scrimba dapat menjadi pendekatan untuk pengembangan alat pembelajaran pemrograman secara lebih luas.

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Section: Pendahuluanunclassified

Penerimaan Pembelajar terhadap Penggunaan Scrimba sebagai Multimedia Pembelajaran Interaktif

Wulan

Setiarini

2021

Edsence

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“…Brinton et al [10] investigated factors that are associated with the decline of activity on MOOC forums, and they used statistical analysis and general model to analyze the forum activity data. Dazo et al [15] aimed to use grain student log data to understand student video viewing behaviors in computer science education.…”

Section: The Literature Reviewmentioning

confidence: 99%

“…Foundation of Computer Science lasted 12 weeks. In each week, instructor of this course released a set of lecture videos (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). About 66% of lecture videos were 3-10 min in length.…”

Section: Participants and Coursementioning

confidence: 99%

A computational investigation of learning behaviors in MOOCs

Zhong

Liu

et al. 2017

Comp Applic In Engineering

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Massive open online courses (MOOCs) are the latest e‐learning initiative to attain widespread popularity in the world. Thus, it is highly required to have a throughout analysis of learning in MOOCs, from theoretical to practical. Our primary goal is to take a detailed and comprehensive investigation into the learning behaviors in MOOCs, as well as to identify issues that have not yet to be adequately resolved. We employed commonly used educational data mining methodologies to analyze and interpret the behaviors in a computer science course based on the questionnaire survey data and daily activity data. We find most of the students could be divided into several groups that are coincident with their learning styles. Moreover, we can easily predict students’ learning styles based on their learning behaviors. This finding means the learning style could be a factor to indicate students’ learning behaviors, or even measure whether a student is appropriate to learn via MOOCs.

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“…On the one hand, video viewing behavior analytics can grant an important advantage in the learning process to understand the use of videos by learners (Ozan and Ozarslan 2016). Relevant studies have been carried out, such as the analysis of video viewing behavior in flipped classrooms (Beatty et al 2017;Dazo et al 2016), learners viewing engagement with in-video quizzes (Kovacs 2016), and identifying learning styles (Dissanayake et al 2018). On the other hand, predicting learner's performance can support both tutors and e-learning systems (Mimis et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining

et al. 2021

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This paper analyzes how learners interact with the pedagogical sequences of educational videos, and its effect on their performance. In this study, the suggested video courses are segmented on several pedagogical sequences. In fact, we’re not focusing on the type of clicks made by learners, but we’re concentrating on the pedagogical sequences in which those clicks were made. We focalize on the interpretation of the path followed by a learner watching an educational video, and the way they navigate the pedagogical sequences of that video, in order to predict whether a learner can pass or fail the video course. Learner’s video clicks are collected and classified. We applied educational data mining technique using K-nearest Neighbours and Multilayer Perceptron algorithms to predict learner’s performance. The classification results are acceptable, the kNN classifier achieves the best results with an average accuracy of 65.07%. The experimental result indicates that learners’ performance could be predicted, we notice a correlation between video sequence viewing behavior and learning performances. This method may help instructors understand the way learners watch educational videos. It can be used for early detection of learners’ video viewing behavior deviation and allow the instructor to provide well-timed, effective guidance.

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An empirical analysis of video viewing behaviors in flipped CS1 courses

Cited by 12 publications

References 12 publications

Penerimaan Pembelajar terhadap Penggunaan Scrimba sebagai Multimedia Pembelajaran Interaktif

Penerimaan Pembelajar terhadap Penggunaan Scrimba sebagai Multimedia Pembelajaran Interaktif

A computational investigation of learning behaviors in MOOCs

Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining

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