Final Project: Insights into Computer-Based Testing and Educational Assessment
In this blog post, I will share insights gained from studying digital assessment, with a focus on Every Student Succeeds Act (ESSA) and the State of Texas Assessment of Academic Readiness (STAAR) program. We will explore technology's impact on assessment practices and strategies for achieving a comprehensive approach to classroom assessment. Through the lenses of data-driven instruction, standards-based grading, and decision-driven data collection, we will investigate how assessment data can improve teaching methods and enhance student learning. Comprehending the fundamental significance of assessments is imperative for educators as it facilitates the alignment of teaching methods with assessment goals and ensures that students are sufficiently equipped for achieving success.
Understanding Assessment Frameworks
In the dynamic landscape of education, educators must stay informed about assessment frameworks and programs to effectively meet their students' varying needs. In the first weeks of this course, we explore the various aspects of educational assessments in the context of ESSA and the STAAR program. It is crucial for educators to understand the details of these frameworks in order to match their teaching approaches with assessment goals and adequately equip students for achievement (U.S. Department of Education, 2016). One aspect of effective educational assessment is the emphasis on high-quality assessments, as emphasized by the ESSA regulations. ESSA advocates for a balanced approach to assessments, rejecting unnecessary or random testing practices in favor of thoughtful evaluation methods (U.S. Department of Education, 2016). This emphasis on finding a balance, reflects on the recognition that assessments should serve as tools for meaningful learning rather than a simple measure of compliance. Moreover, ESSA prioritizes flexibility for states and districts to tailor assessments to cater to the specific needs of diverse student populations while ensuring that students' civil rights are protected and promotes transparency in reporting student progress (U.S. Department of Education, 2016). The Texas Education Agency (TEA), in alignment with ESSA principles, actively leverages this flexibility to make informed decisions that align with the agency's Strategic Plan and foster collaborative decision-making through stakeholder engagement.
The STAAR program has been a fundamental aspect of the Texas education system since it was established in 2012, serving as a thorough evaluation system for assessing students' knowledge and skills (Wikipedia contributors, 2024). The STAAR program includes a wide range of assessments, including online testing that can be adjusted for eligible students (Texas Education Agency-STAAR Resource, 2024a). Significantly, STAAR Alternate 2 provides accommodations for special education students, and Texas English Language Proficiency Assessment System (TELPAS) and TELPAS Alternate cover assessments for English learners. These adjustments guarantee that the STAAR program continues to be fair and accessible, meeting the varying needs of students in Texas.
To facilitate student preparation and familiarization with the online testing environment, the STAAR Online Testing Platform offers tutorials, practice tests, and operational assessments (Texas Education Agency-Testing, 2024a). By providing a comprehensive approach to online testing, the platform aims to establish a secure testing environment while equipping students and staff with the necessary resources for effective online testing experiences (Texas Education Agency-Testing, 2024a).
Embracing Standards-Based Grading
One new and effective method becoming popular in the field of education is Standard-based grading (SBG), as explained by TeacherEase (2019). SBG breaks down the material into smaller, achievable learning goals, emphasizing expertise over completion rates and using a unique grading system to indicate improved proficiency levels (TeacherEase, 2019). This method is important in guaranteeing that students make consistent progress towards a thorough understanding of the curriculum. This approach, exemplified in Seth Battis' YouTube video on standards-based grading using Canvas Outcomes, emphasizes the importance of assessing student progress against specific standards (Battis, 2016). By aligning assessment criteria with predefined learning objectives, educators can provide clear and transparent feedback to students, which is key to making formative assessments effective (Powell, 2012), facilitating a deeper understanding of their strengths and areas requiring further development. Standards-based grading fosters a holistic approach to assessment, enabling educators to tailor instruction to meet individual student needs effectively. As educators, understanding the implications of these assessment practices is paramount. Standard-Based Grading's emphasis on accurate measurement of learning ensures a fair evaluation of students' comprehension of the concepts being taught (TeacherEase, 2019). Furthermore, its specificity and mastery-focused approach offers a practical framework to ensure that students meet the academic standards outlined in assessments such as STAAR.
However, while these readings offer valuable insights, there is a need for further exploration to effectively implement formative assessment and standard-based grading in the classroom (Stiggins, 2005; TeacherEase, 2019). Although Stiggins and TeacherEase discuss the importance of changing the emotional environment, concrete strategies and successful implementation examples would enhance understanding. Moreover, staying updated of the latest research and best practices in formative assessment and standard-based grading is crucial for ongoing improvement and relevance in teaching practices.
Computer-Based Testing in Modern Education
Shifting the focus to computer-based assessments and digital assessment, it is evident that these technological advancements have significantly impacted teaching practices. Dembitzer et al.'s research investigates into the multidisciplinary nature of computer-based assessments, stressing the importance of considering various perspectives, including those of students (Dembitzer et al., 2017). The study highlights how computer-based assessments can cater to different learning styles, essential in making education more inclusive and student-focused (Dembitzer et al., 2017). Similarly, JISC's guide, "Effective Assessment in a Digital Age," praises the transformative potential of technology in assessment practices (Effective Assessment in a Digital Age, 2010). The guide emphasizes the numerous benefits that technology brings to assessment, including efficiency gains, flexibility in assessment design, and the ability to capture a broader extent of learner skills and attributes (Effective Assessment in a Digital Age, 2010).
As an educator, I understand the importance of how computer-based testing and digital assessment have changed the way we teach. Incorporating technology into assessments fits smoothly into the changing education environment, meeting the varied needs of today's students and providing chances for customized, captivating, and successful learning experiences. In order to guarantee fair access to digital assessments, it is essential to tackle issues like technology availability and the possibility of test anxiety.
Data-Driven Instruction
The utilization of computer-based testing demonstrates significant transformative potential, as well as the utilization of data. Paul Bambrick-Santoyo's reading "Driven by Data" thoroughly examines the significance of using data to guide instruction for increasing student success, and outlines key principles and practices that educators should take into account. Bambrick-Santoyo underscores the significance of implementing a structured approach for utilizing data-driven instruction, with an emphasis on four essential principles: assessment, analysis, action, and culture. These principles offer educators a systematic framework for utilizing data in a structured manner to inform instructional decisions and improve student learning achievements. By adhering to these principles, educators can ensure that their instructional practices are grounded in evidence and tailored to accommodate the diverse needs of their students. Additionally, the Abrams, Varier, and Jackson (2016) reading explores how teachers' utilization of assessment data is related to how well they align their instruction with state curriculum standards. This study highlights the challenges educators encounter, such as the lack of connection between assessments and instructional materials, and underscores the significance of providing adequate support to educators in utilizing data effectively.
The success story of Rafael Munoz, in "Driven by Data," serves as a compelling example of the transformative impact of data-driven instruction on student achievement. By receiving personalized assistance and clear direction based on evidence-based strategies, Rafael demonstrates remarkable advancements in academic performance, highlighting the potential of data-driven approaches to drive positive change in students' lives (Bambrick-Santoyo, 2010). Furthermore, the Uncommon Schools videos provide practical examples of how data-driven instructional practices are applied in real educational environments. In Data-Driven Instruction: Leading Analysis Meetings (2012), Jesse Rector and Paul Chin demonstrate the importance of analysis meetings in identifying student challenges and planning targeted interventions. Similarly, in Analysis 2 (2010b), first-grade teachers engage in deep analysis to pinpoint root issues and develop effective instructional strategies. These videos underscore the real-world application of data-driven practices in improving student outcomes. By working together to analyze information and create strategic plans, teachers can use data to constantly improve and promote student success.
Teachers are at the forefront of adjusting their teaching methods to meet the changing needs of their students. A deep understanding of modern educational assessment methods is crucial in this adaptation, as they are essential for measuring student advancement, pinpointing areas in need of growth, and ultimately, improving learning results. Educators must thoroughly investigate different assessment methods in order to effectively navigate the complex landscape of educational assessment (Battis, 2016).
Transformative Potential of Big Data in Education
The rise of technology has transformed education assessment, allowing for innovative methods like computer-based text analysis and automated assessment. Aken's (2017) article presents a systematic framework for categorizing computer-based text analysis tools, highlighting the variety of automated assessment and machine-assisted analysis techniques accessible to educators. These tools streamline assessment processes and provide valuable insights into student learning patterns, enabling educators to make data-driven instructional decisions tailored to their students' needs.
The use of big data analytics has revolutionized educational assessment by collecting and analyzing large amounts of data from digital learning environments. Cope and Kalantzis (2016) highlight big data's transformative potential in education for evidence-based decision-making and personalized learning. By using big data analytics, educators can better understand student learning, target areas for improvement, and optimize interventions for maximum learning outcomes. However, while there are benefits to using technology for assessments, there are also challenges related to academic honesty and the trustworthiness of AI-based assessment instruments. O'Donnell's investigation into Ohio's implementation of computerized grading for state examinations brings up important concerns regarding the reliability and equity of AI-driven evaluation instruments (O'Donnell, 2018). While technology has clear advantages in education, there are doubts among those involved about its capacity to effectively assess complex elements of student work, like creativity and argumentation.
As educators, it is imperative to address these concerns proactively by implementing strategies to uphold academic integrity and mitigate the risk of online cheating. Measures such as randomizing questions, setting time limits, and fostering a culture of academic honesty through clear communication and expectations are essential in maintaining the integrity of online assessments (O'Donnell, 2018). Moreover, an understanding of the ethical considerations surrounding big data analytics is crucial for ensuring responsible data usage and safeguarding student privacy rights (Cope & Kalantzis, 2016).
In the realm of data-driven instruction, educators leverage student data to inform teaching practices and instructional decisions. Bolz and Madhavan define data-driven instruction as the process of collecting, analyzing, and interpreting student data to identify areas of strength and weakness, thereby enabling teachers to tailor instruction to meet individual student needs effectively (Bolz & Madhavan, 2023). Similarly, decision-driven data collection focuses on gathering specific data points that directly inform teaching strategies, curriculum adjustments, and intervention plans (Bambrick-Santoyo, 2010).
Empowering Students through Data and Technology
Navigating the specifics of assessment frameworks and programs is foundational to fostering student success. By embracing the principles of high-quality assessments, educators can cultivate inclusive and engaging learning environments that empower students to thrive. Through ongoing exploration and application of assessment practices, educators are equipped to adeptly navigate the evolving educational landscape, shaping the future of education for generations to come. This involves leveraging data-driven instruction to drive continuous improvement and promote equitable outcomes for all students, as underscored by Bambrick-Santoyo's work and other research studies. Successful implementation of computer-based testing, while holding transformative potential, requires a thoughtful, well-planned approach that considers the needs of both educators and students.
In alignment with these principles, the final project for this course involved analyzing data from a digital assessment using the Canvas Learning Management System. Within Canvas, I selected and aligned 6 outcomes (standards) to a question bank. Subsequently, I utilized these standards to create a quiz. Following the completion of the quiz by students, I started a thorough examination of the collected data and results analysis. By utilizing Canvas's features, such as "Moderate This Quiz," I gained valuable insights into student performance, including completion rates, time taken, attempts remaining, and individual scores. By hovering over students names, I could review their quiz responses, facilitating a deeper understanding of their comprehension. Additionally, the "Quiz Statistics" feature provided a broader overview, showcasing average scores, high and low scores, and average completion times. Furthermore, the quiz summary provided detailed information about each question, highlighting correct responses and areas requiring additional support. This project serves as a practical demonstration of how digital assessments and computer-based assessments can be effectively utilized to track standards-based grading and conduct data analysis, reflecting a commitment to leveraging technology for educational advancement and student success.
Additional Resources:
References
Abrams, L., Varier, D., & Jackson, L. (2016). Unpacking Instructional Alignment: The Influence of Teachers’ Use of Assessment Data on Instruction. Grantee Submission, 34(4), 15–28. https://doi.org/10.18820/2519593X/pie.v34i4.2
Aken, A. (2017) An Evaluation of Assessment-Oriented Computer-Based Text Analysis Paradigms. Higher Education Research, 2(4), 111-116.
Bambrick-Santoyo, P. (2010). Driven by Data: A Practical Guide to Improve Instruction. Jossey-Bass.
Battis, S. (2016, June 7). Standards-Based Grading with Canvas Outcomes [Video]. YouTube. https://www.youtube.com/watch?v=L64TykLXCd4
Bolz, M. J., & Madhavan, V. (2023, October 29). What is Data-Driven instruction in Education? Houghton Mifflin Harcourt. https://www.hmhco.com/blog/what- is-data-driven-instruction
Cope, B., & Kalantzis, M. (2016). Big Data Comes to School: Implications for Learning, Assessment, and Research. AERA Open, 2(2).
Dembitzer, L., Zelikpvitz, S., & Kettler, R. (2017). Designing Computer-Based Assessments: Multidisciplinary Findings and Student Perspectives. International Journal of Educational Technology, 4(3), 20-31
Effective Assessment in a Digital Age: A guide to technology-enhanced assessment and feedback. (2010). https://ctl.utexas.edu/sites/default/files/digiassass_eada.pdf
Mashable Brand X. (2014, September 3). Big data’s making education smarter [Video]. YouTube. https://www.youtube.com/watch?v=K_wAHEHTy-g
O’Donnell, Patrick. (2018, March 19). Computers are now grading essays on Ohio’s state tests. Plain Dealer, The: Web Edition Articles (Cleveland, OH). https://www.cleveland.com/metro/2018/03/computers_are_now_grading_essays_on_ohios_state_tests_your_ch.html
Powell, S. D. (2012). Your introduction to education: Explorations in Teaching. Prentice Hall.
Standards-Based grading. (2019, October 31). The Experiential School of Greensboro. https://theexperientialschoolgso.wordpress.com/about/standards-based-grading/
Stiggins, R. (2005). From Formative Assessment to Assessment for Learning: A Path to Success in Standards-Based Schools. The Phi Delta Kappan, 87(4), 324-328.
TeacherEase. (2019). What is Standards-based Grading? Teacherease.com. https://www.teacherease.com/standards-based-grading.aspx
Texas Education Agency. (2022, August 11). Academic Accountability. https://tea.texas.gov/Student_Testing_and_Accountability/Accountability/State_Accountability/
Texas Education Agency. (2024a, January 8). STAAR Resources. https://tea.texas.gov/student-assessment/testing/staar/staar-resources
Texas Education Agency. (2024, January 9). Testing. https://tea.texas.gov/Student_Testing_and_Accountability/Testing/Testing/
Uncommon Schools. (2012, June 23). Data-Driven instruction: leading analysis meetings [Video]. YouTube. https://www.youtube.com/watch?v=mUd5VJbuD5M
Uncommon Schools. (2010b, April 8). Analysis 2.Mov [Video]. YouTube. https://www.youtube.com/watch?v=nrWJ8cfIQg0
Uncommon Schools. (2010a, April 8). Action.mov [Video]. YouTube. https://www.youtube.com/watch?v=nJeGfP1seEE
Uncommon Schools. (2010, April 8). Culture.mov [Video]. YouTube. https://www.youtube.com/watch?v=PJ6KUyKFpyo
U.S. Department of Education. (2016). Every Student Succeeds Act Assessments under Title I, Part A & Title I, Part B: Summary of Final Regulations. In U.S. Department of Education. https://www2.ed.gov/policy/elsec/leg/essa/essaassessmentfactsheet1207.pdf
Wikipedia contributors. (2024, February 24). State of Texas Assessments of academic readiness. Wikipedia. https://en.wikipedia.org/wiki/State_of_Texas_Assessments_of_Academic_Readiness#:~:text=STAAR%20replaced%20the%20TAKS%20in,continued%20to%20take%20the%20TAKS
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