Reprint of “Conceptual change and knowledge integration as learning processes in higher education: A latent transition analysis”

Flaig, Maja; Simonsmeier, Bianca A.; Mayer, Anne‐Kathrin; Rosman, Tom; Gorges, Johannes; Schneider, Michael

doi:10.1016/j.lindif.2018.07.001

Cited by 10 publications

(16 citation statements)

References 37 publications

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“…It posits a well investigated learning mechanism in school children's acquisition of new concepts. Author like Flaig et al (2018) studied the concepts of human memory of 137 undergraduate psychology longitudinally to note down conceptual changes via the latent student profile changes analysis. The findings of this paper also highlight the significance of the conceptual change, especially the knowledge integration in the higher education platforms, and also exemplify the functionality of the LPTA for modeling the individual differences in the identified knowledge structures.…”

Section: Social Structurementioning

confidence: 99%

Integration of Knowledge in Education

Jarin¹,

Mumu²,

Talukder³

et al. 2021

BJIT

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Based on the citation records, this study for the first time examines the bibliometric characteristics of integration of knowledge in education literature from the year 1985 to June 2020. This study reviews total 102 documents published in the Web of Science index journals. For the visualization and analysis, VOSviewer software was applied. The bibliometric results identified the most productive and impactful authors and journals in this literature. After the science mapping of bibliometric data, this paper identifies the major underlying research themes integration of knowledge in education. The key themes in this literature include the knowledge transfer, higher education, university, innovation and management. Among these keywords, knowledge transfer, higher education and model show the strongest correlation, followed by innovation, commercialization; performance and management; multiple intelligence and information. However, there is no significant correlation among keywords like knowledge transfer, knowledge integration, healthcare, and multiple intelligence. Therefore, future research can be directed focusing on these keywords to expand the existing knowledge in this literature.

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Section: Social Structurementioning

confidence: 99%

Integration of Knowledge in Education

Jarin¹,

Mumu²,

Talukder³

et al. 2021

BJIT

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“…In this type of person-centered analysis, learners are clustered into systematic subgroups according to their measured values on multiple variables that are meant to reflect the learning process. LTA has been used in multiple studies during the last years, for example to study the structure and development of children's knowledge about density and buoyancy force (Edelsbrunner et al, 2015(Edelsbrunner et al, , 2018Schneider & Hardy, 2013), psychology students' understanding of human memory (Flaig et al, 2018), as well as secondary school students' understanding of rational numbers (Kainulainen et al, 2016) and motivational development across multiple subjects (Costache et al, 2021). Moreover, cross-sectional person-centered approaches can be useful to identify learners with certain characteristics, for example learners at risk of developing reading disabilities (Swanson, 2012), low literacy (Mellard et al, 2016), motivational patterns across subjects (Gaspard et al, 2019), patterns of competence levels across multiple skills in science (Schwichow et al, 2020), or maladaptive learning strategies (Fryer, 2017).…”

Section: Person-centered Approaches To Studying Heterogeneity Among Learnersmentioning

confidence: 99%

“…The differences in how these fit indices balance fit, sample size, and model complexity indicate that when comparing the fits of models that differ in the number of extracted profiles/clusters, different model fit indices may indicate different model choices. For example, in the study by Flaig et al (2018), the AIC and adjusted BIC indicated that the five profile model had the best fit to the data, whereas the BIC favored the three-profile solution. These situations are rather the rule than the exception in previous studies using latent transition analysis.…”

Section: Aic)mentioning

confidence: 99%

“…Based on the typical characteristics and results from previous studies in educational research that have used LTA or cross-sectional versions of it, such as latent class (LCA) or latent profile analysis (LPA), (e.g., Flaig et al, 2018;Fryer, 2017;McMullen et al, 2015;Schneider & Hardy, 2013;Straatemeier et al, 2008;, we constructed an applied example. This example serves two purposes: (1) It illustrates the questions researchers are faced with when conducting LTA, and (2) it describes the basic scenario of the current simulation study.…”

Section: Lta In Educational Research: An Applied Examplementioning

confidence: 99%

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A Simulation Study on Latent Transition Analysis for Examining Profiles and Trajectories in Education: Recommendations for Fit Statistics

Edelsbrunner¹,

Flaig²,

Schneider³

2021

Preprint

Self Cite

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Latent transition analysis is an informative statistical tool for depicting heterogeneity in learning as latent profiles. We present a Monte Carlo simulation study to guide researchers in selecting fit indices for identifying the correct number of profiles. We simulated data representing profiles of learners within a typical pre- post- follow up-design with continuous indicators, varying sample size (N from 50 to 1000), attrition rate (none/10% per wave), and profile separation (entropy; from .73 to .87). Results indicate that the most commonly used fit index, the Bayesian information criterion (BIC), and the consistent Akaike information criterion (CAIC) consistently underestimate the real number of profiles. A combination of the AIC or the AIC3 with the adjusted Bayesian Information Criterion (aBIC) provides the most precise choice for selecting the number of profiles and is accurate with sample sizes of at least N = 200. The AIC3 excels starting from N = 500. Results were mostly robust towards differing numbers of time points, profiles, indicator variables, and alternative profiles. We provide an online tool for computing these fit indices and discuss implications for research.

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“…Kesulitan peserta didik dalam memahami konsep cenderung mencerminkan seberapa jauh dan bermakna suatu konsep dipahami dengan baik dan benar (Flaig, dkk., 2018). Informasi terkait level pemahaman konsep dapat menjadi indikasi awal bagi guru untuk memetakan hasil pembelajaran, sekaligus menjadi kilas balik pengembangan strategi instruksional untuk pembelajaran konsep berikutnya (Romine & Sadler, 2016).…”

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Mengevaluasi Level Pemahaman Konsep Hidrolisis Garam Peserta Didik Menggunakan Tes Diagnostik Pilihan Ganda Empat Tingkat

Suteno

Laliyo

Iyabu³

et al. 2021

JPSI

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The ability of students to understand and use concepts is the main key in scientifically explaining various chemical phenomena in nature. This ability develops relatively, often with the development of the learner level. This study aims to understand the level of understanding of the concept of salt hydrolysis of class XI high school students and chemistry students in the I, II, III and IV years and non-chemistry students with a total sample of 875 respondents. The instrument used in this study was a four-level multiple choice test. This diagnostic test combines the measurement of knowledge and reasoning with the level of self-confidence, as a reflection of the level of mastery of concepts. The data analysis technique used is quantitative, using SPSS 25 software, namely the non-parametric test (Kruskal Wallis test), comparing the significance value (Asmp. Sig) with a probability of 0.005. The results showed that the significance value of 0.00 was less than 0.05; That is, there is a significant difference in the level of understanding of the salt hydrolysis concept of students. The level of understanding of the concept of high school students is better than the first, second, third, and fourth year students in chemistry and non-chemistry students. These findings reinforce the story that although students have experienced learning experiences at a higher level, it does not guarantee the development of mastery of understanding the concept of hydrolysis

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Reprint of “Conceptual change and knowledge integration as learning processes in higher education: A latent transition analysis”

Cited by 10 publications

References 37 publications

Integration of Knowledge in Education

Integration of Knowledge in Education

A Simulation Study on Latent Transition Analysis for Examining Profiles and Trajectories in Education: Recommendations for Fit Statistics

Mengevaluasi Level Pemahaman Konsep Hidrolisis Garam Peserta Didik Menggunakan Tes Diagnostik Pilihan Ganda Empat Tingkat

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