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Richard Churches; Kate Wastie; Max Jones; Nina Dhillon – Education Development Trust, 2024
This report provides advice to policymakers and school leaders on the use of assessment centres as part of a teacher selection approach. It discusses the relationship between assessment centre scores prior to joining teaching, and teacher effectiveness over a six-year period. It draws from various stages of a wider research project, the overall…
Descriptors: Beginning Teachers, Classroom Techniques, Prediction, Teacher Selection
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Herman Aksom; Veronika Vakulenko – Teaching Public Administration, 2024
In this conceptual paper, we aim to revisit key research themes in contemporary organizational institutionalism and by doing this, redirect attention of scholars in public administration towards the most promising domains of application of institutional theory. We propose to shift attention from enabling and power-induced framing of institutional…
Descriptors: Organizational Theories, Public Administration, Social Change, Public Sector
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Robert D. Plumley; Matthew L. Bernacki; Jeffrey A. Greene; Shelbi Kuhlmann; Mladen Rakovic; Christopher J. Urban; Kelly A. Hogan; Chaewon Lee; Abigail T. Panter; Kathleen M. Gates – British Journal of Educational Technology, 2024
Even highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co-redesign methods in partnership with disciplinary experts to create high-structure STEM courses that better support students and produce…
Descriptors: Learning Analytics, Prediction, Undergraduate Study, Biology
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Napol Rachatasumrit; Paulo F. Carvalho; Kenneth R. Koedinger – International Educational Data Mining Society, 2024
What does it mean for a model to be a better model? One conceptualization, indeed a common one in Educational Data Mining, is that a better model is the one that fits the data better, that is, higher prediction accuracy. However, oftentimes, models that maximize prediction accuracy do not provide meaningful parameter estimates, making them less…
Descriptors: Data Analysis, Models, Prediction, Accuracy
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Jade Mai Cock; Hugues Saltini; Haoyu Sheng; Riya Ranjan; Richard Davis; Tanja Käser – International Educational Data Mining Society, 2024
Predictive models play a pivotal role in education by aiding learning, teaching, and assessment processes. However, they have the potential to perpetuate educational inequalities through algorithmic biases. This paper investigates how behavioral differences across demographic groups of different sizes propagate through the student success modeling…
Descriptors: Demography, Statistical Bias, Algorithms, Behavior
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Masrai, Ahmed; El-Dakhs, Dina Abdel Salam; Yahya, Noorchaya – SAGE Open, 2022
This study examines the relationship between L2 vocabulary knowledge, self-rating of word knowledge, self-perceptions of four language skills (listening, reading, speaking, and writing), and students' academic achievement. An objective measure of lexical knowledge and questionnaire on self-perception and self-rating of vocabulary knowledge were…
Descriptors: Prediction, Grade Prediction, Academic Achievement, Vocabulary
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Hoq, Muntasir; Brusilovsky, Peter; Akram, Bita – International Educational Data Mining Society, 2023
Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…
Descriptors: Academic Achievement, Prediction, Models, Introductory Courses
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Shen, Guohua; Yang, Sien; Huang, Zhiqiu; Yu, Yaoshen; Li, Xin – Education and Information Technologies, 2023
Due to the growing demand for information technology skills, programming education has received increasing attention. Predicting students' programming performance helps teachers realize their teaching effect and students' learning status in time to provide support for students. However, few of the existing researches have taken the code that…
Descriptors: Prediction, Programming, Student Characteristics, Profiles
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Orhan, Ali – Smart Learning Environments, 2023
This study aimed to investigate the predictive role of critical thinking dispositions and new media literacies on the ability to detect fake news on social media. The sample group of the study consisted of 157 university students. Sosu Critical Thinking Dispositions Scale, New Media Literacy Scale, and fake news detection task were employed to…
Descriptors: Misinformation, Identification, Social Media, College Students
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Wang, Yang – Technology, Knowledge and Learning, 2023
Learning affective state is determinate to online learning. Different affective states are associated with different online learning behaviors. Given the behavioral indicators of different affective states are still to be explored, this study constructed a data-driven online learning affective state detector by analyzing the learning log data of…
Descriptors: Electronic Learning, Affective Behavior, Learning Management Systems, Measures (Individuals)
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Witherby, Amber E.; Carpenter, Shana K.; Smith, Andrew M. – Metacognition and Learning, 2023
Prior knowledge is often strongly related to students' learning. In the present research, we explored the relationship between prior knowledge and the accuracy of students' predictive monitoring judgments (judgments of learning; JOLs) and postdictive monitoring judgments (confidence judgments). In four experiments, students completed prior…
Descriptors: Metacognition, Prior Learning, Accuracy, Prediction
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Ikeda, Kenji – Metacognition and Learning, 2023
This experimental study examined whether the uninformative anchoring effect, which should be ignored, on judgments of learning (JOLs) was eliminated through the learning experience. In the experiments, the participants were asked to predict whether their performance on an upcoming test would be higher or lower than the anchor value (80% in the…
Descriptors: Metacognition, Learning Processes, Evaluative Thinking, Learning Experience
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Liu, Xiaoling; Cao, Pei; Lai, Xinzhen; Wen, Jianbing; Yang, Yanyun – Educational and Psychological Measurement, 2023
Percentage of uncontaminated correlations (PUC), explained common variance (ECV), and omega hierarchical ([omega]H) have been used to assess the degree to which a scale is essentially unidimensional and to predict structural coefficient bias when a unidimensional measurement model is fit to multidimensional data. The usefulness of these indices…
Descriptors: Correlation, Measurement Techniques, Prediction, Regression (Statistics)
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Jung, Rex E.; Hunter, Dan R. – Creativity Research Journal, 2023
Psychologist J. P. Guildford issued a challenge to study creativity nearly 70 years ago. How well have we done and what might the next steps be in our endeavors to understand creativity? The field of creativity research has examined the internal thinking process of creativity, largely through measures of divergent thinking and remote associates.…
Descriptors: Creativity, Creative Thinking, Success, Intelligence
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Yildiz, Banu – International Journal of Psychology and Educational Studies, 2023
Effectively addressing and resolving conflicts in close relationships plays an important role in maintaining healthy relationships. Therefore, in this study, it is aimed to examine the predictive role of attachment styles (anxiety and avoidance) and the growth fear in close relationships on constructive and destructive conflict resolution…
Descriptors: Conflict Resolution, Attachment Behavior, Fear, Anxiety
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