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Smith, Bevan I.; Chimedza, Charles; Bührmann, Jacoba H. – Education and Information Technologies, 2022
Although using machine learning for predicting which students are at risk of failing a course is indeed valuable, how can we identify which characteristics of individual students contribute to their being At-Risk? By characterising individual At-Risk students we could potentially advise on specific interventions or ways to reduce their probability…
Descriptors: Individualized Instruction, At Risk Students, Intervention, Models
Zachary Weingarten; Amy Peterson; Kyle Allen – National Center on Intensive Intervention, 2023
Readiness for change is an important factor in successful implementation and scale-up of a new practice or program in schools. The process of assessing and developing readiness for change is a key feature of the Exploration Stage, which is the first of four implementation stages identified by the Active Implementation Research Network. Data-based…
Descriptors: Data Use, Intervention, Individualized Instruction, Student Needs
Laura Moore; Fionnuala Larkin; Sarah Foley – Journal of Autism and Developmental Disorders, 2024
Autistic adults experience high rates of metal health difficulties and face significant barriers to accessing appropriate mental health care. Empirical research and recent professional guidelines emphasise the importance of modifying standard mental health interventions to best meet the needs of autistic adults. This systematic review explored…
Descriptors: Mental Health, Mental Health Programs, Mental Health Workers, Intervention
Paraskevi Topali; Ruth Cobos; Unai Agirre-Uribarren; Alejandra Martínez-Monés; Sara Villagrá-Sobrino – Journal of Computer Assisted Learning, 2024
Background: Personalised and timely feedback in massive open online courses (MOOCs) is hindered due to the large scale and diverse needs of learners. Learning analytics (LA) can support scalable interventions, however they often lack pedagogical and contextual grounding. Previous research claimed that a human-centred approach in the design of LA…
Descriptors: Learning Analytics, MOOCs, Feedback (Response), Intervention
Meghan Jacquelynn Malloy – ProQuest LLC, 2024
While systems of support are used in schools to assist students' academic growth, parents are not always informed of the support process or the academic goals set for their child (RTI Action Network, n.d.; Troisi, 2014; Weingarten et al., 2020). In the aftermath of the pandemic, students present a wide variety of academic needs (Lewis et al.,…
Descriptors: Parent Attitudes, Parent Participation, Advocacy, Literacy
Katherine Picho-Kiroga; Marcela Valencia Serrano; Noel Bourne; Jerriel Hall – Advances in Engineering Education, 2024
Stereotype threat (ST) is implicated as a contributory factor to attrition in Science Technology Engineering and Math (STEM) fi elds. One of the mechanisms by which ST degrades performance is by impairing metacognitive monitoring (Schmader et al. 2008), which is positively related to learning and performance (Hadwin et al., 2017; Winne &…
Descriptors: Performance, African American Students, Engineering Education, Stereotypes
Qiongjiang Song; Yuhan Liu; Qinggen Zhang – British Educational Research Journal, 2025
This study aimed to contribute to the substantial body of research on critical thinking (CT) interventions by determining whether the effectiveness of two CT interventions (generic and infusion) varied according to students' baseline CT levels. Using a quasi-experimental design, we collected data from two universities, with 167 participants from…
Descriptors: Critical Thinking, Thinking Skills, Skill Development, Teaching Methods
Sara Sanders; Aundrea McFall; Kristine Jolivette – Beyond Behavior, 2024
Sometimes, despite the most perfectly delivered writing lesson implemented with fidelity, students with emotional and behavioral disorders (EBD) do not make the progress expected by teachers or themselves. In these instances, teachers may individualize and differentiate for students with EBD not making adequate progress by purposefully…
Descriptors: Behavior Disorders, Emotional Disturbances, Writing Instruction, Individualized Instruction
Kristina K. Vargo; Christina M. Gushanas – Intervention in School and Clinic, 2024
Students sometimes engage in challenging behaviors that require teachers to select and design appropriate behavior management interventions. Teachers may choose from various evidence-based intervention strategies when addressing students' challenging behaviors. Reinforcement-based strategies are preferred due to their desirable long-term outcomes.…
Descriptors: Punishment, Behavior Change, Classroom Techniques, Evidence Based Practice
Yang, Christopher C. Y.; Ogata, Hiroaki – Education and Information Technologies, 2023
The application of student interaction data is a promising field for blended learning (BL), which combines conventional face-to-face and online learning activities. However, the application of online learning technologies in BL settings is particularly challenging for students with lower self-regulatory abilities. In this study, a personalized…
Descriptors: Individualized Instruction, Learning Analytics, Intervention, Academic Achievement
Magableh, Ibrahim Suleiman; Abdullah, Amelia – International Journal of Evaluation and Research in Education, 2022
Differentiated instruction (DI) is a teaching approach involves several strategies in which teachers adapt, modify, adjust and change instruction to respond to students' diverse individual needs in heterogeneous classrooms. The study aimed at exploring the effectiveness of DI on secondary stage students' proficiency level. The study followed the…
Descriptors: Individualized Instruction, Instructional Effectiveness, Secondary School Students, Reading Comprehension
Catalano, Jennifer; Weirick, Whitney; Hasko, Janna; Antia, Shirin – Journal of Deaf Studies and Deaf Education, 2022
The study examined the effects of a coaching intervention on teachers' ability to implement academically responsive instruction through flexible instructional arrangements in self-contained classrooms for students who are deaf and hard of hearing, as well as the impact of instructional arrangements on students' academic engagement. Using a…
Descriptors: Coaching (Performance), Deafness, Hearing Impairments, Active Learning
Mengjiao Yin; Hengshan Cao; Zuhong Yu; Xianyu Pan – International Journal of Web-Based Learning and Teaching Technologies, 2024
This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a…
Descriptors: Predictor Variables, Artificial Intelligence, Performance, Cluster Grouping
Valarie Algee – Online Learning, 2025
This qualitative study explored the perceptions of 20 online literacy tutors within the United States on the benefits and challenges of implementing synchronous, one-on-one online tutoring for literacy intervention to kindergarten through Grade 6 students and what they feel are elements contributing to success as they engage in this instructional…
Descriptors: Electronic Learning, Literacy, Synchronous Communication, Individualized Instruction
Takami, Kyosuke; Flanagan, Brendan; Dai, Yiling; Ogata, Hiroaki – Smart Learning Environments, 2023
In the age of artificial intelligence (AI), trust in AI systems is becoming more important. Explainable recommenders, which explain why an item is recommended, have recently been proposed in the field of learning technology to improve transparency, persuasiveness, and trustworthiness. However, the methods for generating explanations are limited…
Descriptors: Artificial Intelligence, Personality, Cognitive Processes, Public Health