Influence of an integrated learning diagnosis and formative assessment-based personalized web learning approach on students learning performances and perceptions

While education technology has been widely used in classrooms, and considerable investments have been made to support its use in the UK, the evidence base for many such rapidly changing technologies is weak, and their efficacy is unclear. The aim of this paper is to systematically review and synthesise empirical research on the use of technology in formative assessment, to identify approaches that are effective in improving pupils' learning outcomes. The review involved a search of 11 major databases, and included 55 eligible studies. The results suggest promising evidence that digitally delivered formative assessment could facilitate the learning of maths and reading for young children, but there is no good evidence that it is effective for other subjects, or for older children, or that it is any more effective than formative assessment without technology. The review found no good evidence that learner response systems work in enhancing children's academic attainment, and there is no evidence supporting the effectiveness of such technologies that embed gaming features. Much research in this area is of poor quality. More rigorous studies using causal designs are thus urgently needed. Meantime, there should be no rush to use technology on the basis of improving attainment.

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“…Some platforms have integrated diagnosis and formative assessment which are adaptive to the learners' progress (e.g. Wongwatkit et al 2017).…”

Section: Computer-assisted Formative Assessmentmentioning

confidence: 99%

Is technology always helpful?: A critical review of the impact on learning outcomes of education technology in supporting formative assessment in schools

See

Gorard

et al. 2021

Research Papers in Education

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“…Wu et al, (2017) [6] provided the contents of the lesson after examining whether it had a precedent concept for the learning concept. The AI convergence education algorithm of Wongwatkit et al, (2017) [7] is shown in [Figure 1].…”

Section: Theoretical Background 21 Ai Convergence Education and Individualized Learningmentioning

confidence: 99%

“…Figure 1. Procedure of the personalized web learning system based on the personalized information and formative assessment (Adapted from [7])…”

Section: Learning Style Of Technological Problem Solvingmentioning

confidence: 99%

Learning Style for AI Convergence in Technology Education

Lim¹

2021

JHSTI

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The purpose of this study was to devise a model of learning style for individualized learning of AI convergence in technological problem solving, which is the main teaching-learning method of technology subject. Among the dimensions of recognition-judgment, creativity, execution, attitude, and interaction of technological problem-solving learning styles, the dimensions that act mainly according to the problem content and learning form were selected and constructed as a model. The method of research for this purpose was Kolb's research method, which derives four learning styles by synthesizing the correlation between two types of information perception methods and two types of information processing methods. Therefore, in this study, a creativity-execution model was devised in consideration of the 'adaptation-innovation type' of the creativity level and the 'reflection-action type' of the execution level. In addition, an interactive-attitude model was devised in which the 'independent-cooperative type' in the interaction dimension and the 'avoidance-participation type' in the attitude dimension act dynamically. In addition, a five-factor learning style model for technological problem solving was devised and presented, with all five dimensions of recognition-judgment, creativity, execution, interaction, and attitude of the technological problem-solving learning style as factors. The learner characteristics defined through the model design suggested the direction of AI convergence education considering individual characteristics of learners and provided basic data for it. In the future, it is suggested that specific individualized teaching and learning development research be conducted in AI convergence education, taking into account the characteristics of learners according to the learning style of technological problem solving.

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“…Additionally, there is research that supports the understanding that the technical experience is a determining factor in students perceived quality of their online courses (Li et al, 2010) and that there is disparity in the availability and quality of home Internet access for U.S. students (KewalRamani et al, 2018). Lastly, there is a significant body of research that supports perceptions as a valid construct and a specific call for more research in understanding how perceptions impact student achievement (Horak & Galluzzo, 2017;Wongwatkit et al, 2017). This study combines and adds to the existing body of research in all of the domains addressed in this literature review.…”

Section: Chapter Summarymentioning

confidence: 85%

“…final course grades, participation, attendance, graduation rates, reenrollment). There is still a lack of thorough understanding of how students' perceptions impact their academic achievement (Wongwatkit et al, 2017). Moreover, student perceptions can be studied both quantitatively and qualitatively (Zumbrunn et al, 2016).…”

Section: Student Perceptionsmentioning

confidence: 99%

Impact of Internet Connection on Gifted Students’ Perceptions of Course Quality at an Online High School

Hawthorne

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Online learning is on the rise in K-12 education and, with the lockdowns and social distancing measures implemented as a result of COVID-19, has gained increased prominence. While the demand for online learning is on the rise, many U.S. students lack adequate Internet connectivity to have a successful online learning experience. Connectivity issues, particularly when they impact audio, can cause students to tune out or even drop out of online learning. This is problematic for online schools and course providers who often have no control over the speed of a student’s home Internet connection. Online schools also have to balance student perceptions, which have been linked to their achievement. This mixed methods study examines the role of Internet connectivity on the perceptions of highly gifted students on the quality of their fully online English course on six domains: (a) appeal, (b) challenge, (c) choice, (d) meaningfulness, (e) self-efficacy, and (f) communication. In this study, highly gifted students who attended an online public school (N=19) that utilizes synchronous and asynchronous learning methods reported their perceptions on the six domains of online course quality and also their Internet downloads speed, which were used to divide them into two groups - low and high Internet download speeds. The results of the quantitative survey, a modified version of the Student Perceptions of Classroom Quality (Gentry & Owen, 2004; Gentry & Springer, 2002), were then analyzed based on the two independent groups. No statistically significant difference was found in student perceptions on any of the six domains based on the participant’s Internet download speed. This finding may be limited based on the small sample size available for data analysis in this study. Focus groups supported the findings of the statistical analysis. A total of 12 themes emerged from the focus groups to help explain the students’ perceptions of their online courses. An additional two themes were identified as common technical issues caused by Internet connectivity in online learning and three themes related to troubleshooting technical issues. This study contributes knowledge to the fields of online education, gifted education, impact of student perceptions, and transactional distance in online learning.

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Influence of an integrated learning diagnosis and formative assessment-based personalized web learning approach on students learning performances and perceptions

Cited by 48 publications

References 42 publications

Is technology always helpful?: A critical review of the impact on learning outcomes of education technology in supporting formative assessment in schools

Is technology always helpful?: A critical review of the impact on learning outcomes of education technology in supporting formative assessment in schools

Learning Style for AI Convergence in Technology Education

Impact of Internet Connection on Gifted Students’ Perceptions of Course Quality at an Online High School

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