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A teacher's guide to excellence in every classroom : creating support systems for student success
\"In A Teacher's Guide to Excellence in Every Classroom: Creating Support Systems for Student Success, author John R. Wink acknowledges the unique and significant role that educators play in the lives of their students both as role models and guides. Teachers in the 21st century are far more than simple educators in the lives of their students. As such, this book acts as a guide for educators who wish to maximize their impact in their students' lives and unlock their students' full potential. Readers will not only learn how to increase their effectiveness as educators, but how to push all their students toward academic excellence\"-- Provided by publisher.
Book
Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course
As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students’ learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students’ collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.HighlightsIntegrated approach was used to combine AI with learning analytics (LA) feedbackQuasi-experiment research was conducted to investigate student learning effectsIntegrated approach to foster student engagement, performances and satisfactionsParadigmatic implication was proposed for develop AI-driven learning analyticsClosed loop was established for both AI model development and educational application.
Journal Article
Design thinking in schools : a leader's guide to collaborating for improvement
\"School innovation expert John B. Nash demonstrates how design thinking can be adapted successfully by busy school leaders seeking student-centered solutions to a range of challenges\"-- Provided by publisher.
Book
Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy
There has been debates related to online and blended learning from a perspective of learner experiences in terms of student satisfaction, engagement and performances. In this paper, we analyze student feedback and report the findings of a study of the relationships between student satisfaction and their engagement in an online course with their overall performances. The module was offered online to 844 university students in the first year across different disciplines, namely Engineering, Science, Humanities, Management and Agriculture. It was assessed mainly through continuous assessments and was designed using a learning-by-doing pedagogical approach. The focus was on the acquisition of new skills and competencies, and their application in authentic mini projects throughout the module. Student feedback was coded and analyzed for 665 students both from a quantitative and qualitative perspective. The association between satisfaction and engagement was significant and positively correlated. Furthermore, there was a weak but positive significant correlation between satisfaction and engagement with their overall performances. Students were generally satisfied with the learning design philosophy, irrespective of their performance levels. Students, however, reported issues related to lack of tutor support and experiencing technical difficulties across groups. The findings raise implications for institutional e-learning policy making to improve student experiences. The factors that are important relate to the object of such policies, learning design models, student support and counseling, and learning analytics.
Journal Article
Meta-Analysis and Common Practice Elements of Universal Approaches to Improving Student-Teacher Relationships
Past research has shown student-teacher relationships (STRs) are associated with student outcomes, including improvements in academic achievement and engagement and reductions in disruptive behaviors, suspension, and risk of dropping out. Schools can support STRs universally and systematically by implementing universal, school-wide, and class-wide programs and practices that aim to facilitate high-quality STRs. This study applied meta-analytic and common element procedures to determine effect sizes and specific practices of universal approaches to improving STRs. The universal programs with the largest effects were Establish-Maintain-Restore and BRIDGE. Other programs demonstrated moderate effects in one study, with combined effect sizes revealing smaller effects. The common elements procedure identified 44 practices teachers can implement to promote positive STRs, with 14 proactive and direct practices. Programs with the largest effects, in general, contained the most proactive and direct practices for improving STRs. Implications of these findings and future research recommendations are discussed.
Journal Article
Perspectives on transitions in schooling and instructional practice
\"Perspectives on Transitions in Schooling and Instructional Practice examines student transitions between major levels of schooling, teacher transitions in instructional practice, and the intersection of these two significant themes in education research. Twenty-six leading international experts offer meaningful insights on current pedagogical practices, obstacles to effective transitions, and proven strategies for stakeholders involved in supporting students in transition.
Book
The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher education
Chatbot usage is evolving rapidly in various fields, including higher education. The present study’s purpose is to discuss the effect of a virtual teaching assistant (chatbot) that automatically responds to a student’s question. A pretest–posttest design was implemented, with the 68 participating undergraduate students being randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts). Data was garnered utilizing an academic achievement test and focus groups, which allowed more in depth analysis of the students’ experience with the chatbot. The results of the study demonstrated that the students who interacted with the chatbot performed better academically comparing to those who interacted with the course instructor. Besides, the focus group data garnered from the experimental cohort illustrated that they were confident about the chatbot’s integration into the course. The present study essentially focused on the learning of the experimental cohort and their view regarding interaction with the chatbot. This study contributes the emerging artificial intelligence (AI) chatbot literature to improve student academic performance. To our knowledge, this is the first study in Ghana to integrate a chatbot to engage undergraduate students. This study provides critical information on the use and development of virtual teaching assistants using a zero-coding technique, which is the most suitable approach for organizations with limited financial and human resources.
Journal Article
Absent from school : understanding and addressing student absenteeism
In Absent from School, Gottfried and Hutt offer a comprehensive and timely resource for educators and policy makers seeking to understand the scope, impact, and causes of chronic student absenteeism. The editors present a series of studies by leading researchers from a variety of disciplines that address which students are missing school and why, what roles schools themselves play in contributing to or offsetting patterns of absenteeism, and ways to assess student attendance for purposes of school accountability. The contributors examine school-based initiatives that focus on a range of issues, including transportation, student health, discipline policies, and protections for immigrant students, as well as interventions intended to improve student attendance. Only in the past two or three years has chronic absenteeism become the focus of attention among policy makers, civil rights advocates, and educators. Absent from School provides the first critical, systematic look at research that can inform and guide those who are working to ensure that every child is in school and learning every day -- Provided by publisher.
Book
Factors Influencing Preservice Teachers' Intention to Use Technology: TPACK, Teacher Self efficacy, and Technology Acceptance Model
This study aimed to investigate structural relationships between TPACK, teacher self-efficacy, perceived ease of use, and perceived usefulness for preservice teachers who intend to use technology, based on the Technology Acceptance Model (TAM). A total of 296 responses from the College of Education from three Korean universities were analyzed by employing the structural equation modeling methods. The results indicated that preservice teachers' TPACK significantly affected teacher self-efficacy and perceived ease of using technology. The teachers' TPACK also positively influenced their perceived ease of using technology and perceived usefulness of technology in the classroom. Finally, teacher self-efficacy, perceived ease of use, and perceived usefulness of using technology affected teachers' intention to use technology. However, TPACK did not directly affect their intention to use technology. Based on the findings, we discuss implications and suggest future research directions for preservice teachers' intention to use technology.
Journal Article