Geri Dön

Prediction of course completion based on participants' social engagement on a social-constructivist MOOC platform

Başlık çevirisi mevcut değil.

  1. Tez No: 403337
  2. Yazar: AYŞE SALİHA SUNAR
  3. Danışmanlar: Dr. SU WHITE, Prof. HUGH DAVIS
  4. Tez Türü: Doktora
  5. Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2017
  8. Dil: İngilizce
  9. Üniversite: University of Southampton
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 144

Özet

Özet yok.

Özet (Çeviri)

MOOCs o er world-widely accessible online content typically including videos, readings, quizzes along with social communication tools on a platform that enables participants to learn at their own pace. In 2016, over 58 million people join MOOCs. Far fewer people actually participate in MOOCs than originally sign up and then there is a steady attrition as courses progress. The observation of high attrition has prompted concerns among MOOC providers to mitigate their high attrition rates. Recent studies have been able to correlate social engagement of learners to course completion. Researchers use participants' digital traces to make sense of their engagement in a course and identify their needs to predict future patterns and to make interventions based on these patterns. The research reported here was conducted to further understand learners social engagement on a social-constructivist MOOC platform, the impact of engagement on course completion, and to predict learners' course completion. The ndings of this research show that a commonly known social feature, follow, which is integrated into the Futurelearn MOOC platform has potential value in allowing tracking and analysing the behaviours of participants. The patterns of learners social engagement were modelled and a completion prediction model was developed. This model was successful at predicting those who might complete the course at a high or low success rate. The contributions of this research are that the behaviour chains could be the basis of a personalised recommender system, and the completion model based on social behaviour could contribute to wider prediction model based on a wider range of factors.

Benzer Tezler

  1. Kitlesel açık çevrimiçi kurslarda katılımcıların İngilizce dil gruplarının tespitine dayalı davranış ve performans analizi

    Behaviour and performance analysis of learners identified in English language based groups on massive open online courses

    İSMAİL DURU

    Doktora

    Türkçe

    Türkçe

    2020

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolYıldız Teknik Üniversitesi

    Bilgisayar Mühendisliği Ana Bilim Dalı

    PROF. DR. BANU DİRİ

    DOÇ. DR. GÜLÜSTAN DOĞAN

  2. Çevrimiçi BİT dersindeki öğrenci davranışlarının ve öğrenme performanslarının öğrenme analitiği ile incelenmesi

    An examination of student behaviours and learning performances in online ICT course with learning analytics

    SEMİH ORKUN

    Yüksek Lisans

    Türkçe

    Türkçe

    2024

    Eğitim ve ÖğretimAnkara Üniversitesi

    Bilgisayar ve Öğretim Teknolojileri Eğitimi Ana Bilim Dalı

    PROF. DR. AYFER ALPER

  3. Makine öğrenimi algoritmalarını kullanarak öğrenci akademik performans tahmini

    Student academic performance prediction using machine learning algorithms

    AIGERIM SULTANALI

    Yüksek Lisans

    Türkçe

    Türkçe

    2024

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolGazi Üniversitesi

    Bilişim Sistemleri Ana Bilim Dalı

    PROF. DR. HASAN ÇAKIR

  4. Yelkenli teknelerde tasarım prosedürü

    The Designing procedure of sailing yachts

    İBRAHİM KARATAŞ

    Yüksek Lisans

    Türkçe

    Türkçe

    1992

    Gemi Mühendisliğiİstanbul Teknik Üniversitesi

    DOÇ. DR. ÖMER BELİK

  5. Bitümlü sıcak karışımların deformasyon direncinin üç eksenli kayma mukavemeti deneyi ile incelenmesi

    Investigation of deformation resistance of hot-mixed asphalt mixtures by triaxial shear strength test

    ALTAN ÇETİN

    Doktora

    Türkçe

    Türkçe

    2008

    İnşaat Mühendisliğiİstanbul Teknik Üniversitesi

    İnşaat Mühendisliği Ana Bilim Dalı

    PROF. DR. EMİNE AĞAR