Data collection and assessment for student evaluation is an analytical process that ensures that educators and evaluators get the chance to fully understand and be in the position to reflect and have a better understanding of heir students and what is required for their improvement. When planning any evaluation, the feasibility of various data is an essential step to be considered in the process, and therefore the use of standardized collection data methods and the analytical methods help in the assurance of quality. Evaluation of data should take a routine program because this will ensure an efficient source of information for the evaluation programs. The ideal importance of data relates to the execution of evaluation. The various data forms include the disaggregation of the data, the availability of the data, and the mechanism applied by the educator in the evaluation of the data to set future and planned goals. The essay, therefore, tends to look at the various methods relating to learning evaluation with a view on data collection and its evaluation in finding new and better strategies for the learners.
Methods in Assessment of Data
The best form of collecting and assessing data on students is through observations. Getting to experience the students while presenting or showcasing their learning experience best helps in collecting the information required in the evaluation process. The various observation opportunities present include debates, having group discussions, presentations, and performances. Analysis of these observable experiences greatly helps in a better understanding of the various sets of learning degree that learners have gained in their schooling experiences (Amanatidou, Cunningham, and Cox, 2016).
Various documentation can be a great source of relatable data that can be used in the assessment and evaluation of learners' understanding. The various documents that can be used in the collection of data and its analysis include research papers, exams, standardized knowledge tests, and also through lab reports. The analysis of these documentations greatly helps in a better understanding of various data and information. Educators can use this set of information from these documents to analyze and evaluate the extent by which the students can reflect the information taught in class and whether these kinds of data is the true reflection of their teaching and if they can use this type of data to analyze future lessons.
Student Participation Rates
Participation rates by students provide the best form of data that can be used by educators in the analysis of their student's lesson understanding. The extent to which students participate in their learning experiences can best tell how much learners have understood the learning experiences by the educators. Educators can use this type of information in their analysis and a better understanding of the learner's experience in class.
Description of Data Collection
The most important aspect of selecting the data to be used is by first ensuring that the identified data provides the evidence required in the determination of the degree of the goal and the intended outcome. The data collected should primarily be a reflection of the studies taught and a means to help the educator better analyze their learner's abilities. Data collected through direct methods should ensure that the learners demonstrated their behavior, thought process, and general understanding. Indirect data collected, on the other hand, should demonstrate highly nature towards which the learner reflects upon their thought processes and their behaviors. Observations are, for instance, a form of direct data where the educator gets to preview and see how the learners have understood or reflected upon the studies taught. Participation rates, on the other hand, as a form of indirect data, provides a reflection of the extent by which the students can demonstrate their understanding of the learning and topics given (Suraiya and Maulidian, 2019).
Analysis of the Student Learning
Analysis of these data involves the effective reflection of the educator's mode of having a reflective response on how the students have best understood their learning experience. The analytical view of these concepts is widely applicable in the educator's own reflective view of the learner's experience. Through analysis, the educator is best able to evaluate the state of his learners and be able to draw future learning plans and a better understanding of the state of his students. The analysis allows the educators to better plan for future plans and implements better ways through which they can best understand their learners and device better teaching approaches that will have a further success on the students (Unnikrishnan, Donovan, Macpherson, and Tormey, 2019).
The data will be used for further instructor because first, it informs the educator on the various important points to which there might be a lapse in the learner's understanding. Assessment of data collected better allows the educator to create a learning plan for the future because the information collected comes as an important means to ensuring that learning experiences by the learners are instilled in them and that every aspect of their studies is guided in the right direction. Analysis of the data informs on the future because the reflective aspect of these studies creates a better view of the data and helps the educators prepare early enough and subject their general view on the student's understanding of planning on a better work plan in the future (Singh, 2018).
Suraiya, N., & Maulidian, M. O. R. (2019, December). Development of the Process Evaluation Model in Ips Integrated Learning Based on the 2013-Curriculum. In 4th Annual International Seminar on Transformative Education and Educational Leadership (AISTEEL 2019). Atlantis Press.
Amanatidou, E., Cunningham, P., & Cox, D. (2016). Background Document on P2P evaluation/impact assessment.
Unnikrishnan, S., Donovan, J., Macpherson, R., & Tormey, D. (2019). Machine Learning for Automated Quality Evaluation in Pharmaceutical Manufacturing of Emulsions. Journal of Pharmaceutical Innovation, 1-12.
Singh, J. H. (2018). Front-End Evaluation Planning for e-Learning: A Practical Approach. In Leading and Managing e-Learning (pp. 281-304). Springer, Cham.
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