How Learning Has Been Personalised: A Review of Literature from 2009 to 2018

Li, Kam Cheong

doi:10.1007/978-3-030-21562-0_6

Cited by 14 publications

(9 citation statements)

References 18 publications

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“…The thesis that the development of a personalized learning environment should be aimed not only at student performance in academic parameters but also at creating a transformative environment conducive to personal development coincides with the conclusions of this study [36,37]. The authors support the opinion that there is a need to explore how educators use data, how technologies are created to support learners, and how the content and curriculum are developed to support individual learning [14].…”

Section: Comparative Perspectivesupporting

confidence: 69%

Personalized Computer Support of Performance Rates and Education Process in High School: Case Study of Engineering Students

et al. 2021

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To date, the existing principles of a unified approach to schooling do not meet the requirements of a fundamental socio-economic transition characterized by personalization and customization of products and services that concern all areas, including education. The paradigm of education systems is being restructured in many countries towards a new approach of personalized learning that is adapted to students' needs, allows them to choose individual educational trajectories, and respects unique skills and qualities. The study aimed to determine the impact of personalization on the academic performance of engineering students in physics. The experiment on personalized student support was conducted during the academic year 2018-2019 at I.T. Trubilin Kuban State Agrarian University. The sample covered 78 students. The starting point of the research was the assessment of students' skills in mathematics, as the study of physics is based on mathematical analysis and interpretation. Test in physics and the criterion of alpha Kronbach (test reliability is determined with a coefficient of 0.87) were used as a toolkit for research. Besides, the statistical tools and covariance analysis of data processing were employed. The research was supported by the SPSS Statistics software package. According to the results obtained, higher post-test scores were recorded for students in the experimental group. The personalized learning has been found to have a significant impact on the academic performance of students. Those with low and medium ability in mathematics demonstrated higher growth in academic achievement than students with recorded high aptitude scores. A correlation between learning methods and mathematical skills to achieve high academic performance has been established. The influence of personalization in the learning process was determined to be moderately high (determinacy coefficient of 72.6%). The study is relevant for the development of constructive approaches that could help teachers improve their approach to learning and students improve academic achievements. The results may be of interest to researchers of educational technology, educators, and university administration management.

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Section: Comparative Perspectivesupporting

confidence: 69%

Personalized Computer Support of Performance Rates and Education Process in High School: Case Study of Engineering Students

et al. 2021

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“…The results show the emerging trends of the features of smart learning reported in the literature, such as collaborative learning, adaptivity, self-regulated learning, experience-based learning and ubiquitous learning. Such features are not unique to smart learning but are highly interrelated with that widely shown in other technology-enhanced learning approaches such as personalised learning (Li & Wong, 2019a) and mobile learning (Li & Wong, 2019b). Although it is common for educational practices to incorporate various approaches, the interrelationship between smart learning and other related approachestheoretically, conceptually, methodologically and empiricallyshould be an important area for future investigation.…”

Section: Discussionmentioning

confidence: 93%

“…• Smart learning environments: The learning environments where smart learning was implemented consist of the classroom, online environment, blended environment, laboratory. • Smart learning features: The features of smart learning were adapted from the categorisation proposed by Li and Wong (2019a). They consist of rich learning resources, adaptivity, contextawareness, augmented reality, personalised content, personalised learning support, intelligent recommendation, timely intelligent feedback, real-time student feedback, facilitation of interaction, collaborative learning, self-regulated learning, experience-based learning and ubiquitous learning.…”

Section: Data Coding and Categorisationmentioning

confidence: 99%

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Review of smart learning: Patterns and trends in research and practice

Li¹,

Wong

2021

AJET

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This article presents a review of the literature on smart learning in order to provide a comprehensive overview of its latest developments in research and practice. The review covered 90 studies published from 2010 to 2019, which were collected from three publication databases, namely Web of Science, Scopus and ProQuest. They were analysed for the patterns and trends in terms of publication years, sources of publication, countries or regions of publication, research purposes, research methods, educational levels of the studies, application domains, research issues, research participants, learning devices or tools, learning environments and learning features. Overall, the findings show that increasing global attention was given to smart learning from diverse disciplines and contexts of application. The results also reveal the areas which should be addressed in future work: development of pedagogies which make effective use of smart learning technologies in different learning environments; examination of smart learning to cope with learners’ needs in informal contexts; in-depth analysis of how smart learning could be tailored to fit the characteristics of various application domains; and investigation of the views and perceptions of teachers on smart learning technologies, their readiness to use these technologies and their challenges and needs for support.

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“…Our literature review revealed an increasing number of publications related to PL. In Li and Wong ( 2019 )’s systemic review of 179 studies from 2009 to 2018, there was a 120% increase in the number of studies from the 2009 to 2013 period to the 2014–2018 period. Our search of literature after 2019 revealed exponentially increased results.…”

Section: Literature Reviewmentioning

confidence: 99%

Differences in personalized learning practice and technology use in high- and low-performing learner-centered schools in the United States

Lee

Huh

Lin

et al. 2021

Education Tech Research Dev

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The Every Student Succeeds Act supports personalized learning (PL) to close achievement gaps of diverse K-12 learners in the United States. Implementing PL into a classroom entails a paradigm change of the educational system. However, it is demanding to transform traditional practice into a personalized one under the pressure of the annual standardized testing while it is unclear which PL approaches are more likely to result in better academic outcomes than others. Using national survey data of ELA teachers in identified learner-centered schools, this study compared high and low-performing learner-centered schools (determined by their standardized test results) in terms of their use of five PL features (personalized learning plan, competency-based student progress, criterion-referenced assessment, project- or problem-based learning, and multi-year mentoring) and their use of technology for the four functions of planning, learning, assessment, and recordkeeping. Generally, teachers in high-performing schools implemented PL more thoroughly and utilized technology for more functions than those in low-performing schools. Teachers in high-performing schools more frequently considered career goals when creating personal learning plans, shared the project outcomes with the community, and assessed non-academic outcomes. They stayed longer with the same students and developed close relationships with more students. Also, they more frequently used technology for sharing resources and reported having a more powerful technology system than those in low-performing schools. This study informs educators, administrators, and researchers of which PL approaches and technology uses are more likely to result in better academic outcomes measured by standardized assessments.

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How Learning Has Been Personalised: A Review of Literature from 2009 to 2018

Cited by 14 publications

References 18 publications

Personalized Computer Support of Performance Rates and Education Process in High School: Case Study of Engineering Students

Personalized Computer Support of Performance Rates and Education Process in High School: Case Study of Engineering Students

Review of smart learning: Patterns and trends in research and practice

Differences in personalized learning practice and technology use in high- and low-performing learner-centered schools in the United States

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