Online courses recommendation system based on industry occupation skills requirements

Wang, Hai-Hui; Chootong, Chalothon; Ochirbat, Ankhtuya; Sommool, Worapot; Gunarathn, W K T M; Shih, Timothy K.

doi:10.1109/umedia.2017.8074083

Cited by 3 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similarly, Song [76] used Machine Factorization, Su et al [91] used big data analytics, Jain [108] utilized random forests, classification tree, k-nearest neighbors, logistic regression. Along with that, Wang et al [93] used clustering techniques, Zhang e.t al. [113] utilized improved apriori algorithm, [145] Xia used vector space model (VSM) and finally Mondal et.…”

Section: Knowledge-based Filtering (Kbf)mentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Knowledge-based Filtering (Kbf)mentioning

confidence: 99%

“…proposed big data analytics technique. Wang et al[93] used clustering algorithm. Furthermore, Jain[108] used knearset neighbor, decision tree and CN2 rule induction, Zhang et al[113] used Apriori algorithm with Spark model and Xia[145] used vector space mode (VSM) to achieve course recommendations.…”

mentioning

confidence: 99%

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Sistem rekomendasi banyak digunakan sebagai salah satu cara dalam pemberian informasi yang lebih tepat bagi user. Beberapa penelitian sebelumnya menghasilkan sistem rekomendasi di bidang kesehatan [8], youtube [9], industri [10] , pendidikan [11] dan lainnya [12], [13].…”

Section: Sistem Rekomendasiunclassified

Generator Kuis untuk Meningkatkan Hasil Belajar Siswa dalam Smart LMS

Murad

Malik

2021

Journal of Information System

View full text Add to dashboard Cite

Penelitian ini bertujuan mengembangkan salah satu fitur Learning Management System (LMS) terkait online assessment yaitu kuis. Salah satu fitur didalam LMS ini adalah tersedianya fasilitas kuis bagi mahasiswa yang dapat dilakukan sesuai jadwalnya secara online. LMS saat ini hanya menampilkan capaian pengerjaan kuis mahasiswa dari segi nilai. Belum tersedia informasi terkait kesalahan jawaban beserta jawabannya sehingga mahasiswa tidak bisa mengukur capaiana pembelajaran mereka berdasarkan kuis. Menggunakan pendekatan machine learning, penelitian ini membangun sebuah model untuk auto-generate soal-soal kuis yang mengacu kepada lecture note yang sudah tersedia di LMS sesuai matakuliah yang berjalan. Hasilnya terbukti meningkatkan antusias mahasiswa didalam mengerjakan kuis berdasarkan uji coba pada beberapa matakuliah di Jurusan Sistem Informasi, bersumber pada lecture note, soal kuis berhasil di auto-generate per topik matakuliah tersebut. Selain menginformasikan nilai kuis mahasiswa, sistem LMS juga memunculkan jawaban yang salah beserta kunci jawabannya. didapatkan sebuah model guna pengembangan generator kuis.Kata kunci: Smart LMS, machine learning, lecture note, auto-generate, generator kuis

show abstract

“…Seperti recommendation system (Murad et al, 2020), question generator (Wijanarko et al, 2018) dan Internet of Thing (Wang et al, 2017) (Salman et al, 2017). Namun, secara umum penyelenggaraan pembelajaran dengan mode online dijalankan menyesuaikan kebijakan masing-masing negara.…”

unclassified

Teknologi Baru Pada Pendidikan Tinggi Menuju Revolusi Industri 4.0: Studi Kasus Indonesia dan Malaysia

Murad

Hassan

et al. 2021

J. Sistem Info. Bisnis

View full text Add to dashboard Cite

IoT with E-learning is intended to support data collection from devices and share to other devices in use for effective E-learning applications from Smart Campus. This study aims to conduct studies related to online learning models by utilizing Internet of Everything (IoE) technology to support Revolution 4.0. This study aims to support the latest communication paradigm in which the objects of everyday life will be equipped with a series of appropriate protocols and enable them to communicate well with each other as part of the internet. IoE will help improve learning by leveraging the large subject data generated by these objects to provide dynamic services to educators, learners, and even content developers. Using qualitative research methods This research uses a questionnaire to find out the views and assessments of the community in this case online learners regarding online learning as one of the impacts of the Covid-19 pandemic and produces an online learning model that is supported by an integrated system between learning media such as LMS and devices. others use IoE. The results of this study support the implementation of Smart campuses that allow the use of IoE methodologies to make them always ready in certain network areas.

show abstract

Online courses recommendation system based on industry occupation skills requirements

Cited by 3 publications

References 14 publications

A Systematic Mapping Review on MOOC Recommender Systems

A Systematic Mapping Review on MOOC Recommender Systems

Generator Kuis untuk Meningkatkan Hasil Belajar Siswa dalam Smart LMS

Teknologi Baru Pada Pendidikan Tinggi Menuju Revolusi Industri 4.0: Studi Kasus Indonesia dan Malaysia

Contact Info

Product

Resources

About