Longitudinal effect of a computer-based graduated prompting assessment on students’ academic performance

Zhang, Ren-Cheng; Lai, Hui-Min; Cheng, Po‐Wen; Chen, Chin-Pin

doi:10.1016/j.compedu.2017.03.016

Cited by 13 publications

(14 citation statements)

References 35 publications

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“…A further development in recent times has been an increasing use of computerised forms of dynamic assessment (McNeil 2016;Poehner, Zhang, and Lu 2015;Resing, Tunteler, and Elliott 2015;Zhang et al 2017), even involving the use of 3D immersive environments (Passig, Tzuriel, and Eshel-Kedmi 2016). One of the major contributions of computerised approaches is that these can utilise tangible materials that can lead to the capture of a mass of data (e.g.…”

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confidence: 99%

Dynamic assessment: a case of unfulfilled potential?

2018

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confidence: 99%

Dynamic assessment: a case of unfulfilled potential?

2018

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“…Several studies have developed computer-based assessment, called computerized dynamic assessment (Ku, Shih, & Hung, 2014;Lin, 2016;Poehner & Lantolf, 2013;Teo, 2012;Ting & Kuo, 2016;Wang & Chen, 2016;Wu, Kuo, & Wang, 2017;Zhang, Lai, Cheng, & Chen, 2017). The system of computer-based assessment relies upon cake format, in which mediation is provided during the test administration.…”

Section: Computer-based Assessment For Reading Literacymentioning

confidence: 99%

“…If a student answered incorrectly again, the third more explicit prompt was shown. This process continued until either a student answered correctly or a student obtained all four mediating prompts (Poehner & Lantolf, 2013;Teo, 2012;Wang & Chen, 2016;Zhang et al, 2017). For the multiple-choice questions with five answers, there were a total of four mediating prompts in each item (Poehner & Lantolf, 2013).…”

Section: Different Promptings Of Computer-based Assessmentmentioning

confidence: 99%

Effects of prompting type and learning achievement on reading literacy of ninth graders

2022

KJSS

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This paper sought to explore the effects of different prompting types of computer-based assessment on reading literacy of ninth graders and to examine interaction effect between different promptings of computer-based assessment and learning achievement on reading literacy. This research developed computer-based assessment called computerized dynamic assessment (C-DA) that integrated promptings with assessment to support reading literacy. A quasi-experimental design was adopted. 541 ninth graders from 11 secondary schools participated in this study and each individual was randomly assigned into instructional-based prompt (n = 148), error-explanation prompt (n = 139), mixed prompt (n = 131), and verification prompt or control group (n = 123). The results revealed that: (1) there was a statistically significant difference in reading literacy gain score among the prompting groups. Control group scored significantly lower when compared with students in experimental groups; and (2) there was no significant interaction between the two factors, prompting conditions and levels of learning achievement, on reading literacy. The analysis of main effects showed that levels of learning achievement had no effect on reading literacy gain score, whereas promptings of computer-based assessment had a significant impact on students' gain score. Prompting-based groups also received the higher scores when compared to the control condition. As a result, this study provides empirical evidence for educators to make use of the assessment as an effective tool for assessing reading literacy of ninth graders with a wide range of learning achievement in classroom.

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“…R. C. Zhang, H. M. Lai, P. W. Cheng, and C. P. Chen, [10] presented Computer-based Graduated Prompting Assessment System (CGPAS) that is designed through feedback to support 2D graphing. This study includes three different contributions such as first, the TML-based assessment was developed and tracked because the assessment system has become the dominant mode for communicating with learners.…”

Section: Literature Reviewmentioning

confidence: 99%

Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction

Deepika*¹,

Sathyanarayana²

2019

IJIES

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Now-a-days, research in educational mining focuses on modelling student's performance. Many universities include large volumes of data related to student's details, performance, management details, educational process, and etc. Moreover, most of the data remains unused because inability of the university administration to handle it, also huge volumes of data are difficult to perform. In this paper, hybrid Feature Selection (FS) method namely Relief-F and Budget Tree-Random Forest (RFBT-RF) is proposed for selecting active features to reduce high dimensionality and handle uncertainty of data. The proposed feature selection method selects only relevant features instead of selecting redundant and irrelevant features for the classifiers. Also, RFBT-RF method is applied on multiple classifiers like Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) for predicting the Student Academic Performance (SAP). RFBT-RF method was applied on three databases such as UCI (maths), UCI (Portuguese) and Collected dataset. Results showed that, RFBT-RF algorithm achieved 6.85% of improved SAP accuracy compare to the existing Logistic Regression (LR) model.

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Longitudinal effect of a computer-based graduated prompting assessment on students’ academic performance

Cited by 13 publications

References 35 publications

Dynamic assessment: a case of unfulfilled potential?

Dynamic assessment: a case of unfulfilled potential?

Effects of prompting type and learning achievement on reading literacy of ninth graders

Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction

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