Exploring the vector space model for online courses

Shaptala, Roman; Kyselova, Anna; Kyselov, Gennadiy

doi:10.1109/ukrcon.2017.8100370

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Hmedna et al [72] classified learners into groups based on learning styles using supervised learning in order to provide learning contents to the learner. Shaptala et al [92] used VSM with cosine distance, Chakraborty et al used clustering and kmeans [106], and Cooper et al used sequential pattern mining [116] for resource recommendations. Similarly, Chang et al [73] used watch time log for video recommendation.…”

Section: Rq1 How Many Studies Supported Their Claim With Experiments and Which Datasets Were Used In The Studies?mentioning

confidence: 99%

“…LDA was also used to achieve MOOC recommendation by Zarra et al [110], while k-mean clustering in Li et al [118], and context-aware factorization machine algorithm were used by Chanaa and Faddouli [134] in a personalized learning path. Furthermore, Resource recommenders using machine learning included tag recommender using support vector machines and random forest were utilized by Babinec and Srba [84], VSM with cosine distance by Shaptala et al [92]. Furthermore, clustering and k-means for learning resource in Chakraborty et al [106], Cooper et al [116] utilized sequential pattern mining and Chang et al [73] used watch time log for video recommendation.…”

Section: Knowledge-based Filtering (Kbf)mentioning

confidence: 99%

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A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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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.

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Section: Rq1 How Many Studies Supported Their Claim With Experiments and Which Datasets Were Used In The Studies?mentioning

confidence: 99%

Section: Knowledge-based Filtering (Kbf)mentioning

confidence: 99%

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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“…Salah satu bagian yang berperan dalam distance learning adalah computer based e-testing atau ujian yang berbantukan komputer. Penelitian Shaptala dkk [2] memberikan rekomendasi pembelajaran jarak jauh dengan menggunakan model vektor, namun peneltian tersebut tidak membahas penilaian ujian terutama ujian essay, karena penilaian ujian esay masih sulit untuk dilakukan. Penelitian Anang dkk membuat sistem ujian berbantukan komputer untuk penerimaan siswa baru, namun penelitian ini hanya membahas tentang perspektif perekayasaan perangkat lunak yang dibutuhkan karena ada batasan biaya, tempat, dan resource yang ada [3].…”

Section: Pendahuluanunclassified

Perancangan Sistem Pemeriksaan Ujian Essay Menggunakan Sistem Temu Kembali Informasi Model Ruang Vektor

Wahyudi

Akbar

Suryamen

2021

TEKNOSI

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Perkembangan Sistem Pendidikan Jarak Jauh ( Distance Learning) di indonesia tidak lepas dari dukungan sarana dan prasarana yang semakin baik. Salah satu bagian yang berperan dalam Distance learning adalah computer based e-testing atau ujian yang berbantukan komputer. Pada dasarnya konsep ujian essay menggunakan sistem temu kembali informasi model ruang vektor dibuat menyerupai konsep ujian essay manual. Jawaban yang diberikan oleh pengguna akan dibandingkan Similarity Coefficient (kesamaan) nya dengan jawaban yang sudah dimasukkan penguji. Cara untuk menentukan nilai Similarity Coefficient adalah dengan menggunakan model ruang vektor yaitu soal dan jawaban tersebut di umpamakan sebagai vektor sehingga diketahui nilai term frequency (tf) dan inverse term frequency (idf) , dari nilai tf dan idf-nya barulah dapat diketahui nilai Similarity Coefficient (SC).Dari hasil pengujian yang dilakukan dapat disimpulkan bahwa dalam sistem Ujian Essay sistem hanya mampu menilai kebenaran jawaban berdasarkan kata yang sama yang terdapat dalam jawaban referensi, Urutan kata dalam jawaban tidak berpengaruh terhadap penilaian dalam Ujian Essay, Semakin panjang jawaban referensi maka ketelitian sistem akan semakin tinggi begitu juga sebaliknya. Akurasi yang dihasilkan oleh sistem sekitar 75 % - 89 % dengan rata – rata kesalahan 17%.

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“…There is also a lack of standardized datasets available for the evaluation of recommender systems in MOOCs. Researchers have mostly used publicly available datasets of Coursera, edX, and, in some cases, datasets from their own institutes to evaluate recommender systems (Aryal et al, 2019;Dai et al, 2017;Kardan et al, 2017;Mi & Faltings, 2016a;Shaptala et al, 2017;Yang, Adamson, et al, 2014;Yang, Piergallini, et al, 2014). Other authors have created datasets (Onah & Sinclair, 2015a;He et al, 2017;Iniesto & Rodrigo, 2019;Zhou et al, 2015).…”

Section: Future Directionsmentioning

confidence: 99%

Recommender Systems for MOOCs: A Systematic Literature Survey (January 1, 2012 – July 12, 2019)

Khalid

Lundqvist

Yates

2020

IRRODL

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In recent years, massive open online courses (MOOCs) have gained popularity with learners and providers, and thus MOOC providers have started to further enhance the use of MOOCs through recommender systems. This paper is a systematic literature review on the use of recommender systems for MOOCs, examining works published between January 1, 2012 and July 12, 2019 and, to the best of our knowledge, it is the first of its kind. We used Google Scholar, five academic databases (IEEE, ACM, Springer, ScienceDirect, and ERIC) and a reference chaining technique for this research. Through quantitative analysis, we identified the types and trends of research carried out in this field. The research falls into three major categories: (a) the need for recommender systems, (b) proposed recommender systems, and (c) implemented recommender systems. From the literature, we found that research has been conducted in seven areas of MOOCs: courses, threads, peers, learning elements, MOOC provider/teacher recommender, student performance recommender, and others. To date, the research has mostly focused on the implementation of recommender systems, particularly course recommender systems. Areas for future research and implementation include design of practical and scalable online recommender systems, design of a recommender system for MOOC provider and teacher, and usefulness of recommender systems.

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Exploring the vector space model for online courses

Cited by 6 publications

References 2 publications

A Systematic Mapping Review on MOOC Recommender Systems

A Systematic Mapping Review on MOOC Recommender Systems

Perancangan Sistem Pemeriksaan Ujian Essay Menggunakan Sistem Temu Kembali Informasi Model Ruang Vektor

Recommender Systems for MOOCs: A Systematic Literature Survey (January 1, 2012 – July 12, 2019)

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