Creating Better Learning Environments for Students with Trauma

ISTE Coaching Standard 4.5b reads: “Build the capacity of educators, leaders, and instructional teams to put the ISTE Standards into practice by facilitating active learning and providing meaningful feedback”. Using the AI Tool, Scite, I posed the following idea, “how does ISTE Coaching Standard 4.5b apply to students affected by trauma”? Scite identified several articles […]

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Models to Measure Students’ Learning in Computer Science

As computer science becomes integrated into K-12 education systems worldwide, educators and researchers continuously search for effective methods to measure and understand students’ learning levels in this field. The challenge lies in developing reliable and comprehensive assessment models that accurately and discreetly gauge student learning. Teachers must assess learning to support students’ educational needs better. …

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Empowering Students with Goal-Setting and Progress Tracking using Duolingo

According to the U.S. Department of Education (2018), when students set learning goals, they are more likely to be motivated and gain self-management skills, further enhancing their academic achievements. The importance of goal-setting is also evident in research conducted by Bursztyn et al. (2020), which revealed that task-based goals can significantly improve completion rates and overall course performance among college…

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Catering to the Needs of Trauma-Affected Students through Technology

ISTE Coaching Standard 4.5 Professional Learning Facilitator reads, “Coaches plan, provide and evaluate the impact of professional learning for educators and leaders on the use of technology to advance teaching and learning”. Standard 4.5c, expands upon this, and reads: Evaluate PD Impact– Evaluate the impact of professional learning and continually make improvements to meet the […]

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Potential of LLMs and Automated Text Analysis in Interpreting Student Course Feedback

Integrating Large Language Models (LLMs) with automated text analysis tools offers a novel approach to interpreting student course feedback. As educators and administrators strive to refine teaching methods and enhance learning experiences, leveraging AI’s capabilities could unlock more profound insights from student feedback. Traditionally seen as a vast collection of qualitative data filled with sentiments, …

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Integrating Formative AI to interpret student data: Case of World Language Class

Question: How can Edtech coaches assist world language teachers in interpreting qualitative and quantitative student data using formative AI?  Solution:  In world language classes, understanding and interpreting quantitative and qualitative student data could provide an overview of student progress and challenges, enhancing the effectiveness of instruction. Quantitative data offers objective benchmarks of progress and proficiency, allowing for tracking learning outcomes over time. Qualitative data adds…

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