Curriculum-Based Assessment serves as a resource for data collection that could help a teacher evaluate the quality of their instructional interventions. The approach is based on Curriculum-Based Measurement probes that are distributed among students to assess a number of skills. Furthermore, Curriculum-Based Assessment is built on the premise that measurement data is beneficial for teaching intervention enhancement (Wagner et al., 2017). A sample of the probes along with the guide that explains their usage can be found on the North Platte Public Schools website. The data gathered in this way could help a teacher examine the effects of their instructional interventions (Wagner et al., 2017). The Curriculum-Based Measurement sample also provides a scale organized by grade level that assists in the interpretation of the results.
Students’ cumulative files serve as a source of information about a person’s educational life – the analysis of this data reservoir may be viewed as building a linear account of a student’s progress. The access to student’s cumulative files is limited and requires undergoing several administrative procedures. Moreover, it presents an individualized record of improvement or decline, which may be laborious if a more general picture that includes more than one student needs to be built. Cumulative files give a teacher perspective concerning the overarching effects of their practices, helping to reflect on the recurring ones. Additionally, these files may indicate a point where a student’s grades transformed that a teacher may correlate with changes in their instructions.
Cumulative files give a teacher perspective concerning the overarching effects of their practices, helping to reflect on the recurring ones.
Selected Response Assessment
Selected response assessment is directed at determining the degree to which a student is knowledgeable within a topic and whether they gained control over it. This method may be viewed as a somewhat traditional assessment system that functions on the basis of asking questions (Cope & Kalantzis, 2015). The website of Florida Center for Instructional Technology offers an example of assessment items as well as a data analysis worksheet to facilitate the information interpretation collected with the method. Digital technologies have altered the standard hand-written tests and led to computer adaptive tests that became a helpful tool in organizing and analyzing the assessment data. Information extracted in this way can show a teacher the gaps in the students’ knowledge that should be covered and determine what methods were successful or not based on the results from specific sections.
Student rating instruments are a reliable source of information about the effectiveness of a teacher’s instruction. Even though research (Linse, 2017) has demonstrated “a low to moderate positive correlation between students’ ratings and their grades or expected grades” (p. 7), some are skeptical about the instrument. In this way, the data should be analyzed cautiously without a rash conclusion. The site of EducationWorld proposes a number of templates for student surveys; this instrument may help a teacher revise their instructional practices and see shortcomings in their teacher-student interactions.
Self-assessment may not wholly correspond with a student’s performance; in some instances, it even indicates a negative correlation. Therefore, in the analysis of self-assessment data, the lack of association between actual knowledge and perceived one should be considered. This source could be used to accumulate data on the relationship between a student’s performance and their self-assessment or performed, for instance, at the beginning, and by the end of a semester (Cope & Kalantzis, 2015). Student self-assessment could help a teacher gain a more individualized approach to classroom management and understand their students’ educational needs.
Student self-assessment could help a teacher gain a more individualized approach to classroom management.
Cope, B., & Kalantzis, M. (2015). Sources of evidence-of-learning: Learning and assessment in the era of big data. Open Review of Educational Research, 2(1), 194–217.
Linse, A. R. (2017). Interpreting and using student ratings data: Guidance for faculty serving as administrators and on evaluation committees. Studies in Educational Evaluation, 54, 94–106.
Wagner, D. L., Hammerschmidt-Snidarich, S. M., Espin, C. A., Seifert, K., & McMaster, K. L. (2017). Pre-service teachers’ interpretation of CBM progress monitoring data. Learning Disabilities Research & Practice, 32(1), 22–31.