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The Future of Education 9th Edition 2019

Application of Spreadsheets and Neural Networks for Assessing the Knowledge and Skills of Distance Learning Students

Tsvetan Tsvetkov

Abstract

One feature of distance learning in Operations Management is the need for a frequent verification of the students’ knowledge and skills. This is important both for students and lecturers. The students need to be able to find out where they are and make the necessary adjustments. The lecturers should encourage students to work intensively throughout the whole semester. At the same time they have to evaluate the progress of the students and decide whether to take remedial action. Thirdly, ensuring an intensive two-way connection between the student and the lecturer is of particular importance for guaranteeing successful learning. At the same time, lecturers are very busy persons. They are highly involved in both teaching activities, research projects, administrative activities and the like. In many cases, students are many – sometimes a few hundred. In such situations, verifying the work of students is a real challenge. This report provides a method for automating the evaluation of students' written papers. The method is the result of a summary of the author’s experience in distance education for Business Administration students in the subject of Operations Management. The method uses two tools. The first one requires development of a specific form in spreadsheet software. The student completes the form by performing the required calculations, presents the answer and explains how he has solved the tasks. Every task contains one parameter that is dependent on the last few digits of each student's faculty number. Thus tasks are different for each student. When done, the student sends his file through the distance learning platform. Through a simple VBA program the teacher summarizes the results obtained. A model was developed to calculate the correct answer for each task and compare it with the student's answer. Logically, the software can only evaluate the correctness of the calculations, but not the text responses. This is the lecturer’s responsibility. The second tool of the presented method requires development of a neural network model. For this purpose, it is necessary for the lecturer to have accumulated sufficient number of evaluated students’ papers with their answers and the scores. The neural network can be “trained” with the array of students’ work. The more evaluated papers exist, the more accurate the results will be. The lecturer can ask the model to grade the students’ papers and compare the models’ scores with his own evaluations. If the rate of matching is high enough, the lecturer may use the model for further student papers evaluation.

Keywords: Students’ papers evaluation, distance learning, spreadsheets, neural network;

References:


[1] Charu C. Aggarwal, Neural Networks and Deep Learning. A Textbook. Springer, 2018.
[2] Laurene Fausett, Fundamentals of Neural Networks. Architectures, Algorithms, and Applications, Pearson, 1993.

 


Publication date: 2019/06/28
ISBN: 978-88-85813-45-8
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