A Holistic Approach to Assessment: Integrating Self, Peer, Instructor, and AI
As educators, finding better ways to measure learning is always on our agenda. While we’re familiar with traditional assessment methods and the valuable insights they offer in measuring students’ mastery of specific content knowledge, we recognize their limitation in capturing the full range of students’ abilities and experiences, such as critical thinking, creativity, and collaboration. Thus, we acknowledge the necessity of complementary approaches, such as holistic assessment, to gain a more comprehensive understanding of student learning and growth. As illustrated in the following diagram (Figure 1), holistic assessment draws upon multiple sources of evaluation. This means that instead of relying only on one type of assessment, such as exams or quizzes, educators employ diverse assessment methods to gather feedback from multiple sources. This approach encompasses instructor feedback, self-assessment, peer evaluation, and AI analysis. Instructor assessment offers expert guidance and feedback, reflecting the educator’s insights into students’ progress. Self-assessment empowers students to reflect on their own learning, fostering metacognitive skills and self-awareness (Yan et al., 2023). Peer assessment encourages collaborative learning and provides valuable insights from classmates. And, integrating AI-generated evaluation, such as the feedback provided by ChatGPT, adds a new dimension to assessment by offering personalized insights and assistance customized to individual student needs (Chang et al., 2023). Together, these diverse assessment methods contribute to a holistic approach that considers cognitive, affective, and social dimensions of learning, which may ultimately lead to enhanced learning experience for students.
Figure 1
Integrated Holistic Assessment Framework
To illustrate the practical application and efficacy of holistic assessment, let me share an experience that indicates the significance of integrating diverse assessment methods in the classroom. Last semester, I introduced a collaborative research project as part of the course syllabus. This assignment aimed to cultivate an environment of collaboration among students while enhancing their inquiry skills and research abilities. To investigate the effectiveness of diverse assessment methods, I organized group research projects. In these projects, students conducted independent research on selected topics and then presented their findings to the class. After the presentations, each group was expected to participate in both self-reflection and peer evaluation. Groups were tasked with assessing various aspects of their performance, including organization, delivery, engagement, collaboration, creativity, time management, and overall learning derived from their projects. Additionally, students were supposed to use the feedback and critique provided by ChatGPT, supplemented by the personalized notes and suggestions provided by the instructor about the projects.
After evaluating the projects and grading them, students demonstrated a great satisfaction with the assessment process. The feedback from the majority of students highlighted its fairness and quality, with many expressing genuine excitement about their learning experience. This enthusiasm went beyond mere satisfaction, as they appreciated the opportunity to identify areas for improvement and devise strategies to address their weaknesses in the future.
I believe this holistic approach to assessment resonated strongly with most students for several reasons. First, self-assessment empowered students to take ownership of their learning journey and allowed them to develop a sense of accountability and self-awareness. According to the Self-Determination Theory (Deci & Ryan, 2012), when individuals perceive themselves as autonomous agents with the authority to make decisions and feel accountable for their actions, they are more likely to experience intrinsic motivation in their endeavors. Second, the collaborative discussions promoted transparency, fairness, and objectivity in the assessment process. The peer assessment component allowed students to synthesize diverse perspectives, gaining valuable insights from their peers’ feedback and refining their knowledge and skills accordingly. Actively engaging in peer assessment also facilitated the development of critical thinking and communication skills among students. Additionally, ChatGPT’s feedback played a pivotal role by offering concise and direct personalized feedback (Chang et al., 2023). It seems that the interactive engagement with ChatGPT not only sparked students’ curiosity but also provided a non-biased evaluation of their work. T (e.g., Figure 2). Finally, through instructor’s feedback, students identified their strengths and weaknesses and understood the significance of incorporating constructive criticism in their evaluation process.
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Figure 2
An Example of ChatGPT Feedback on Student Performance
In conclusion, the integration of self-assessment, peer evaluation, insights from AI-generated chatbots, and instructor feedback can make the assessment process dynamic and inclusive. This holistic approach not only ensures active involvement of students in evaluating their own work and that of their peers but also fosters transparency and fairness in grading. Furthermore, students receive real-time feedback about their progress, success criteria, and future learning plans. Research conducted by Hattie & Clark (2019) and Hattie & Timperley (2007) underlines the significant impact of such feedback mechanisms in narrowing knowledge gaps and improving learning outcomes.
Despite the challenges that may arise in implementing holistic assessment in the classroom, such as the need for additional time and resources for feedback and reflection, it is crucial to recognize the transformative potential of this approach to assessment in education. Implementing holistic assessment can be manageable and straightforward with proper planning and support. For example, instructors can provide criteria and rubrics for self and peer assessments, offer reflection opportunities, and suggest effective prompts for meaningful interaction with ChatGPT. Additionally, in large classes, utilizing the university’s Learning Management System such as Moodle or Canvas can be invaluable. These platforms can be used to disseminate appropriate resources, schedule workshops or tutorials for hands-on guidance, and encourage peer collaboration through collaborative projects. By nurturing a culture of holistic assessment, we not only encourage students to take ownership of their learning journey but also foster a deeper understanding of the material and cultivate a lifelong passion for learning. With our collective expertise and dedication to education, we can overcome these challenges and make transformative learning a reality for all.
References
Black, P., & Wiliam, D. (2009). Developing the Theory of Formative Assessment. Educational Assessment, Evaluation and Accountability, 21(1), 5–31. https://doi.org/10.1007/s11092-008-9068-5
Chang, D. H., Lin, M. P.-C., Hajian, S., & Wang, Q. Q. (2023). Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization. Sustainability (Basel, Switzerland), 15(17), 12921. https://doi.org/10.3390/su151712921
Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (pp. 416–436). Sage Publications Ltd. https://doi.org/10.4135/9781446249215.n21
Hattie, J., & Clarke, S. (2019). Visible learning: feedback / John Hattie and Shirley Clarke. Routledge, Taylor & Francis Group.
Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Yan, Z., Wang, X., Boud, D., & Lao, H. (2023). The Effect of Self-assessment on Academic Performance and the Role of Explicitness: A Meta-analysis. Assessment & Evaluation in Higher Education, 48(1), 1–15. https://doi.org/10.1080/02602938.2021.2012644
Shiva Hajian-Moghadam
Since 2016, Shiva has been studying the types of instructional prompts and feedback leading to development of inquiry competencies such as scientific observation, global and local testing, data collection, pattern recognition, and inference making within Simulation Assisted Inquiry Learning (SAIL) environments. Learn more about her here.
