Effects of a peer tutor recommender system (PTRS) with machine learning and automated assessment on vocational high school students' computer application operating skills

Abstract

Many information technology courses frequently use the learning by doing strategy in vocational high schools. Particularly, learning computer application operating skills is essential for students because excellent computer application operating skills can help them attain good jobs. However, when fostering students' computer application operating skills by teaching in vocational high school using the learning by doing strategy, a teacher learns that helping all students, evaluating their learning problems, and providing feedback to correct their mistakes are challenging. After investigating the challenge, a machine-learning-based peer tutor recommender system (MPTRS) with automated assessment was proposed to enhance students' learning performance in computer application operating skills. The advanced automated assessment system (AAS) used computer vision technology to evaluate student assignments and instantly return feedback. The recommendation mechanism of the MPTRS enhanced mutual help among students based on their social relationships, learning performance, and recommendation feedback. Furthermore, machinelearning techniques were used to improve recommendations. In the experiment, the experimental group used the proposed system, and the control group used a

The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

“…1 Student [Aarthi et al, 2019, Angelova et al, 2018, Baskota and Ng, 2018, Bhumichitr et al, 2017, Bourkoukou et al, 2017, Cárdenas-Cobo et al, 2020, Chang et al, 2011, Chen et al, 2020, Dahdouh et al, 2019, Praserttitipong and Srisujjalertwaja, 2018, Dussan-Sarria and Leon-Guzman, 2012, Dwivedi and Roshni, 2017, Esteban et al, 2020, Ezz and Elshenawy, 2020, Heras et al, 2020, Hidayat et al, 2020, Hoic-Bozic et al, 2016, Hsu et al, 2013, Iatrellis et al, 2017, Ibrahim et al, 2017, Ibrahim et al, 2020, Isma'il et al, 2020, Janpla and Kularbphettong, 2016, Kanika et al, 2019, Kopeinik, 2018, Liao et al, 2020, Lin et al, 2018, Liu et al, 2017, Ma et al, 2020, Mokarrama et al, 2020, Nho et al, 2020, Nuswantari et al, 2020, Obeid et al, 2018, Palilingan and Batmetan, 2018, Pardos and Jiang, 2020, Pariserum Perumal et al, 2019, Pereira et al, 2018, Porcel et al, 2020, Putri and Zulkarnain, 2...…”

“…[ Aarthi et al, 2019, Baskota and Ng, 2018, Bhumichitr et al, 2017, Praserttitipong and Srisujjalertwaja, 2018, Dwivedi and Roshni, 2017, Esteban et al, 2020, Ezz and Elshenawy, 2020, Ferreira et al, 2020, Heras et al, 2020, Iatrellis et al, 2017, Isma'il et al, 2020, Janpla and Kularbphettong, 2016, Jiang et al, 2014, Lin et al, 2018, Ma et al, 2020, Mokarrama et al, 2020, Porcel et al, 2020, Sabic and El-Zayat, 2010, Salehi et al, 2014, Samin and Azim, 2019, Selvam et al, 2020, Sobecki, 2012, Uslu et al, 2016, Vialardi Sacín et al, 2009, Yuliansyah et al, 2020, Zhu et al, 2018, Zhu et al, 2020 33.75% 4 User's activity (navigation, clicks) [Bai and Yang, 2019, Bourkoukou et al, 2017, Cárdenas-Cobo et al, 2020, Hidayat et al, 2020, Hoic-Bozic et al, 2016, Nho et al, 2020, Peralta et al, 2018, Salehi et al, 2014, Sirikayon et al, 2018, Syed and Nair, 2018, Zhuhadar et al, 2009 13.75% 5 Historical academic data (enrollments, cour...…”

“…[ Cobos et al, 2013, García et al, 2009, Karga and Satratzemi, 2018, Karga and Satratzemi, 2019, Zervas et al, 2015 6.25% 7 User's ratings and feedback [Benhamdi et al, 2017, Cárdenas-Cobo et al, 2020, Hidayat et al, 2020, Karga and Satratzemi, 2018, Karga and Satratzemi, 2019, Ma et al, 2020, Porcel et al, 2020, Zhuhadar et al, 2009 10.00%…”

“…Higher education [Almutairi et al, 2017, Angelova et al, 2018, Benhamdi et al, 2017, Bhumichitr et al, 2017, Bourkoukou et al, 2017, Cárdenas-Cobo et al, 2020, Cerna, 2020, Chen et al, 2020, Cobos et al, 2013, Dahdouh et al, 2019, De Medio et al, 2020, Praserttitipong and Srisujjalertwaja, 2018, Dussan-Sarria and Leon-Guzman, 2012, El-Bishouty et al, 2014, Esteban et al, 2020, Faisal et al, 2020, Ferreira et al, 2020, Heras et al, 2020, Hoic-Bozic et al, 2016, Iatrellis et al, 2017, Ibrahim et al, 2020, Isma'il et al, 2020, Jun Liu et al, 2010, Kanika et al, 2019, Kopeinik, 2018, Lin et al, 2018, Liu et al, 2017, Ma et al, 2020, Nho et al, 2020, Nuswantari et al, 2020, Pardos and Jiang, 2020, Pariserum Perumal et al, 2019, Pereira et al, 2018, Porcel et al, 2020, Putri and Zulkarnain, 2020, Sabic and El-Zayat, 2010, Salehi et al, 2014, Sauer et al, 2014, So...…”

“…To recommend: , Baskota and Ng, 2018, Mokarrama et al, 2020, Obeid et al, 2018, Yuliansyah et al, 2020 13.75% Degree programs, careers, majors or departments [Chang et al, 2011, Ezz and Elshenawy, 2020, Janpla and Kularbphettong, 2016, Nuswantari et al, 2020, Selvam et al, 2020 Collaborators in R&D projects [Wang et al, 2017] 4 To recommend jobs or personalized curriculum [Liu et al, 2017, Palilingan andBatmetan, 2018] 2.50% 5 To recommend learning paths [Iatrellis et al, 2017, Jiang et al, 2014 2.50% 6 Other [Angelova et al, 2018, Faisal et al, 2020, Ma et al, 2020, Sauer et al, 2014 5.00%…”