The Role of Big Data Analytics on Improving Technical and Vocational Education Outcomes

EasyChair Preprint 10434

Abstract

This systematic literature review examines the potential of big data analytics to improve technical and vocational education and training (TVET) outcomes. A search of relevant databases yielded 22 studies that were included in the review. The findings suggest that big data analytics interventions can improve TVET outcomes, particularly in terms of student performance and engagement. However, the quality of the evidence is limited by the high risk of bias in many of the studies and the heterogeneity of the interventions and outcomes measured. Big data analytics interventions can provide real-time insights into student learning behaviors and performance, predict student outcomes, and offer personalized feedback and support. The positive impact of these interventions on TVET outcomes is consistent with previous research in other educational contexts. To maximize the potential of big data analytics in TVET, future research should use rigorous study designs, consistent outcome measures, and identify factors that contribute to the success or failure of these interventions. In conclusion, this review suggests that big data analytics can improve TVET outcomes, particularly where there is a strong culture of data use. Further research is needed to identify factors that contribute to the success or failure of big data analytics interventions in TVET, with the goal of improving TVET quality and enhancing graduate employability.

Keyphrases: Big Data Analytics, Educational Data Mining, Technical and Vocational Education and Training (TVET), learning outcomes, student performance

@booklet{EasyChair:10434,
  author    = {Admas Abtew and Zerihun Olana},
  title     = {The Role of Big Data Analytics on Improving Technical and Vocational Education Outcomes},
  howpublished = {EasyChair Preprint 10434},
  year      = {EasyChair, 2023}}