Education week 4 assignment

Week4assingment.docx

icon The Search & Annotated Bibliography

Directions:

The goal of this assignment is to develop your critical thinking and research skills by evaluating and synthesizing information from different sources on a specific topic. You will assess the relevance, accuracy, and quality of each source and reflect on how it contributes to your understanding of the topic. The overall objective is to ensure you develop a broad range of research-based interpretive understanding of your topic. You are to review a minimum of three scholarly articles related to your research topic.

1. Select Your Sources

· Choose three credible and relevant sources. These can be academic journal articles, books, or reputable reports.

· Ensure the sources are related to your research topic and provide a range of perspectives or insights.

2. Write the Citation (Current APA Style)

· For each source, begin with a full citation using the current APA format.

· Ensure your citations are complete and properly formatted.

3. Summarize the Source (100-150 words per source)

· Provide a concise summary of the key points, arguments, or findings of the source.

· Highlight the author’s main thesis and any relevant details that contribute to your research topic.

4. Evaluate the Source (100-150 words per source)

· Critically evaluate the credibility and reliability of the source.

· Consider the author's qualifications, publication venue, and date of publication.

· Assess the quality of the evidence provided, including whether it is backed by data, case studies, or peer-reviewed research.

· Reflect on any potential bias, gaps, or limitations in the source.

5. Reflect on the Source's Relevance (100-150 words per source)

· Explain how this source is relevant to your research.

· Discuss how the information contributes to your understanding of the topic, and how you might use it in your paper or project.

· Compare this source with others you’ve found and explain whether it confirms, contradicts, or adds new perspectives.

6. Organize the Bibliography

· List the three sources in alphabetical order according to the author’s last name.

· Ensure consistency in formatting and structure throughout the bibliography.

image1.png

_Week3Assignment-v1.docx

Running head: THE BASED OF PREDICTIONS & RATIONALE 2

THE BASE OF PREDICTIONS & RATIONAL 2

The Base of Predictions & Rationale

St Thomas University

EDU 530 Educational Measurement

February 2, 2025

The Base of Predictions & Rational

The growing use of digital technologies in education informs my understanding of AI's potential in formative assessment. AI is used in educational technology to deliver real-time feedback, tailor learning, and help instructors. According to my coursework and readings, formative evaluation allows instructors to detect knowledge gaps, measure progress, and alter teaching (Sortwell et al., 2024). However, AI's ability to improve formative assessment faces severe practical difficulties. Many instructors have challenges using AI-driven formative assessment systems because of insufficient training, equity issues, and technical limits (Børte et al., 2023). AI can create individualized feedback on a scale, but its quality and trustworthiness compared to human review are disputed.

One of my primary assumptions is that AI-embedded formative assessment will enhance K-12 individualized feedback and decision-making. Previous research has shown that AI can assess student replies, discover trends, and customize feedback to specific learning requirements (Weigand et al., 2024). AI-driven assessment systems might save instructors from grading and assessing student work, letting them concentrate on teaching and student engagement. However, AI may depersonalize education and reduce instructors' participation in formative evaluation. I assume that AI models are bias-free, yet research reveals that many AI systems inherit biases from their training data, which might lead to inequitable evaluation results (Dolata et al., 2023). Identifying bias in AI models raises questions about how AI-generated feedback can affect diverse student groups, especially underprivileged ones.

The prediction is that AI-embedded formative assessment systems will reduce teacher effort as they improve. Formative assessment is time-consuming. Therefore, AI's capacity to automate processes like grading multiple-choice questions, delivering quick feedback, and creating progress reports should help instructors save time. However, instructors must still analyze AI-generated insights and ensure that feedback meets students' requirements. Higher-order thinking abilities like critical analysis and creativity are frequently best assessed via open-ended replies and classroom discussions. AI may not be able to do so. While AI has shown potential in essay scoring and natural language processing, its capacity to deliver nuanced feedback on complicated student work needs additional study (Sortwell et al., 2024). AI will complement teacher-driven formative evaluation, not replace it.

One of my uncertainties regarding AI-embedded formative evaluation is my long-term impact on student engagement and motivation. While real-time feedback may help students improve, AI-driven assessment may reduce intrinsic motivation. Impersonal or mechanistic AI comments may deter students. AI-driven formative assessment systems may favor digitally savvy youngsters, creating a digital divide between students with and without home devices. Research suggests equitable access and teacher support enhance digital formative assessment (Borte et al., 2023). These worries suggest further research into how AI might boost student engagement and digital equity.

My academic interests include education, technology, and assessment techniques. So, I am inspired by AI in assessment, and as someone interested in new teaching and learning approaches, personalized training and assessment using AI-driven technologies intrigues me. Teachers and students need to know how AI can improve formative assessment, which impacts student learning. I would like to understand how AI can help teachers with their decision-making while using technologies for education. When AI is integrated into education, ethics, and practical challenges arise: data privacy, risk of algorithmic bias, human assessment supervision, and others must be addressed. The research question I stated previously is “How could AI-embedded formative assessment improve personal feedback and decision-making in K-12 education, and what does that indicate for the workload and burden of teachers?” The research question has a transparent meaning for K-12 education and policy. Formative assessment with AI might revolutionize schools with scalable, tailored learning. However, educators and technology developers should collaborate to solve AI implementation challenges and make these tools effective and equitable. Also, this research may contribute to education technology discussions by examining how AI-driven formative assessment influences teacher and student learning. This work may inspire professional development programs to help educators employ AI-based assessment systems. As teachers' training delays technology uptake in education (Borte et al., 2023), practical professional development approaches are essential for this research.

I aim to raise awareness of how AI may enhance formative assessment for equity, teacher burden, and student engagement. I want to answer my research question to produce ethical and practical AI-driven formative assessments. However, this work may shape future schooling to ensure AI supports teachers. Education must critically assess its effects to guarantee that AI breakthroughs match educational best practices and student learning objectives.

References

Borte, K., Lillejord, S., Erduran, S., Wasson, B., & Greiff, S. (2023). Prerequisites for teachers’ technology use in formative assessment practices: A systematic review. Educational Research Review, 100568–100568. https://doi.org/10.1016/j.edurev.2023.100568

Dolata, M., Katsiuba, D., Wellnhammer, N., & Schwabe, G. (2023). Learning with Digital Agents: An Analysis based on the Activity Theory. Journal of Management Information Systems, 40(1), 56–95. https://doi.org/10.1080/07421222.2023.2172775

Sortwell, A., Trimble, K., Ferraz, R., Geelan, D. R., Hine, G., Ramirez-Campillo, R., Bastian Carter-Thuiller, E. Gkintoni, & Xuan, Q. (2024). A systematic review of meta-analyses on the impact of formative assessment on K-12 students’ learning: Toward sustainable quality education. Sustainability, 16(17), 7826–7826. https://doi.org/10.3390/su16177826

Weigand, H.-G., Trgalova, J., & Tabach, M. (2024). Mathematics teaching, learning, and assessment in the digital age. ZDM. https://doi.org/10.1007/s11858-024-01612-9

Week4assingment.docx

icon The Search & Annotated Bibliography

Directions:

The goal of this assignment is to develop your critical thinking and research skills by evaluating and synthesizing information from different sources on a specific topic. You will assess the relevance, accuracy, and quality of each source and reflect on how it contributes to your understanding of the topic. The overall objective is to ensure you develop a broad range of research-based interpretive understanding of your topic. You are to review a minimum of three scholarly articles related to your research topic.

1. Select Your Sources

· Choose three credible and relevant sources. These can be academic journal articles, books, or reputable reports.

· Ensure the sources are related to your research topic and provide a range of perspectives or insights.

2. Write the Citation (Current APA Style)

· For each source, begin with a full citation using the current APA format.

· Ensure your citations are complete and properly formatted.

3. Summarize the Source (100-150 words per source)

· Provide a concise summary of the key points, arguments, or findings of the source.

· Highlight the author’s main thesis and any relevant details that contribute to your research topic.

4. Evaluate the Source (100-150 words per source)

· Critically evaluate the credibility and reliability of the source.

· Consider the author's qualifications, publication venue, and date of publication.

· Assess the quality of the evidence provided, including whether it is backed by data, case studies, or peer-reviewed research.

· Reflect on any potential bias, gaps, or limitations in the source.

5. Reflect on the Source's Relevance (100-150 words per source)

· Explain how this source is relevant to your research.

· Discuss how the information contributes to your understanding of the topic, and how you might use it in your paper or project.

· Compare this source with others you’ve found and explain whether it confirms, contradicts, or adds new perspectives.

6. Organize the Bibliography

· List the three sources in alphabetical order according to the author’s last name.

· Ensure consistency in formatting and structure throughout the bibliography.

image1.png

_Week3Assignment-v1.docx

Running head: THE BASED OF PREDICTIONS & RATIONALE 2

THE BASE OF PREDICTIONS & RATIONAL 2

The Base of Predictions & Rationale

St Thomas University

EDU 530 Educational Measurement

February 2, 2025

The Base of Predictions & Rational

The growing use of digital technologies in education informs my understanding of AI's potential in formative assessment. AI is used in educational technology to deliver real-time feedback, tailor learning, and help instructors. According to my coursework and readings, formative evaluation allows instructors to detect knowledge gaps, measure progress, and alter teaching (Sortwell et al., 2024). However, AI's ability to improve formative assessment faces severe practical difficulties. Many instructors have challenges using AI-driven formative assessment systems because of insufficient training, equity issues, and technical limits (Børte et al., 2023). AI can create individualized feedback on a scale, but its quality and trustworthiness compared to human review are disputed.

One of my primary assumptions is that AI-embedded formative assessment will enhance K-12 individualized feedback and decision-making. Previous research has shown that AI can assess student replies, discover trends, and customize feedback to specific learning requirements (Weigand et al., 2024). AI-driven assessment systems might save instructors from grading and assessing student work, letting them concentrate on teaching and student engagement. However, AI may depersonalize education and reduce instructors' participation in formative evaluation. I assume that AI models are bias-free, yet research reveals that many AI systems inherit biases from their training data, which might lead to inequitable evaluation results (Dolata et al., 2023). Identifying bias in AI models raises questions about how AI-generated feedback can affect diverse student groups, especially underprivileged ones.

The prediction is that AI-embedded formative assessment systems will reduce teacher effort as they improve. Formative assessment is time-consuming. Therefore, AI's capacity to automate processes like grading multiple-choice questions, delivering quick feedback, and creating progress reports should help instructors save time. However, instructors must still analyze AI-generated insights and ensure that feedback meets students' requirements. Higher-order thinking abilities like critical analysis and creativity are frequently best assessed via open-ended replies and classroom discussions. AI may not be able to do so. While AI has shown potential in essay scoring and natural language processing, its capacity to deliver nuanced feedback on complicated student work needs additional study (Sortwell et al., 2024). AI will complement teacher-driven formative evaluation, not replace it.

One of my uncertainties regarding AI-embedded formative evaluation is my long-term impact on student engagement and motivation. While real-time feedback may help students improve, AI-driven assessment may reduce intrinsic motivation. Impersonal or mechanistic AI comments may deter students. AI-driven formative assessment systems may favor digitally savvy youngsters, creating a digital divide between students with and without home devices. Research suggests equitable access and teacher support enhance digital formative assessment (Borte et al., 2023). These worries suggest further research into how AI might boost student engagement and digital equity.

My academic interests include education, technology, and assessment techniques. So, I am inspired by AI in assessment, and as someone interested in new teaching and learning approaches, personalized training and assessment using AI-driven technologies intrigues me. Teachers and students need to know how AI can improve formative assessment, which impacts student learning. I would like to understand how AI can help teachers with their decision-making while using technologies for education. When AI is integrated into education, ethics, and practical challenges arise: data privacy, risk of algorithmic bias, human assessment supervision, and others must be addressed. The research question I stated previously is “How could AI-embedded formative assessment improve personal feedback and decision-making in K-12 education, and what does that indicate for the workload and burden of teachers?” The research question has a transparent meaning for K-12 education and policy. Formative assessment with AI might revolutionize schools with scalable, tailored learning. However, educators and technology developers should collaborate to solve AI implementation challenges and make these tools effective and equitable. Also, this research may contribute to education technology discussions by examining how AI-driven formative assessment influences teacher and student learning. This work may inspire professional development programs to help educators employ AI-based assessment systems. As teachers' training delays technology uptake in education (Borte et al., 2023), practical professional development approaches are essential for this research.

I aim to raise awareness of how AI may enhance formative assessment for equity, teacher burden, and student engagement. I want to answer my research question to produce ethical and practical AI-driven formative assessments. However, this work may shape future schooling to ensure AI supports teachers. Education must critically assess its effects to guarantee that AI breakthroughs match educational best practices and student learning objectives.

References

Borte, K., Lillejord, S., Erduran, S., Wasson, B., & Greiff, S. (2023). Prerequisites for teachers’ technology use in formative assessment practices: A systematic review. Educational Research Review, 100568–100568. https://doi.org/10.1016/j.edurev.2023.100568

Dolata, M., Katsiuba, D., Wellnhammer, N., & Schwabe, G. (2023). Learning with Digital Agents: An Analysis based on the Activity Theory. Journal of Management Information Systems, 40(1), 56–95. https://doi.org/10.1080/07421222.2023.2172775

Sortwell, A., Trimble, K., Ferraz, R., Geelan, D. R., Hine, G., Ramirez-Campillo, R., Bastian Carter-Thuiller, E. Gkintoni, & Xuan, Q. (2024). A systematic review of meta-analyses on the impact of formative assessment on K-12 students’ learning: Toward sustainable quality education. Sustainability, 16(17), 7826–7826. https://doi.org/10.3390/su16177826

Weigand, H.-G., Trgalova, J., & Tabach, M. (2024). Mathematics teaching, learning, and assessment in the digital age. ZDM. https://doi.org/10.1007/s11858-024-01612-9

Week4assingment.docx

_Week3Assignment-v1.docx

Week4assingment.docx

image1.png

_Week3Assignment-v1.docx

Week4assingment.docx

image1.png