ABSTRACT
Education during pandemics was disrupted by the social distancing restrictions that were imposed by authorities. In this context, education was moved online together with all the knowledge assessment mechanisms like online examination. Oral online exams are good methods to examine students but it requires a lot of resources like time and tutors which usually universities cannot afford to allocate. Usually, exam results must be provided in a few days from the exam date and session periods are short. Written online examinations are prone to cheating due to the availability of multiple communication channels between students or students or third parties. Several cases were reported by tutors and they had to be discussed in the faculty’s management board. Expelling students from universities for cheating reasons may result in serious financial shortages. The only feasible way is to keep away students from cheating in online exams. One potential solution in this sense is to generate unique sets of subjects for each student and to allocate a limited amount of time for solving each subject. Thus, the solution of one student will not be reusable by others. On the other hand, the time limit will restrict the capacity of communication between students when supervised by video cameras. The video cameras must be at least two: one for identifying the student's face and the other focused on the written paper. To generate unique exam subjects, we propose an ion process to infer a template and a synthesis process where the template is instantiated with data computed from random numbers. The approach was tested on a group of 29 students and no cheating incidents were reported. © 2021, National Defence University - Carol I Printing House. All rights reserved.
ABSTRACT
During the COVID-19 pandemic the educational processes were interrupted in most of the countries in the world. Governments switched from face-to-face education to online sessions using learning management systems and video and audio communication tools. Pupils connect to online classes using laptops, tablets, and sometimes using mobile phones. These methods are exhausting and tiring for the pupils, so a limit of half of the time used in the face-to-face classes was set for the online classes. This approach restricts the number of hours a tutor works directly with their students. Probably the theoretical aspects are presented to the class and then a limited set of examples are given. Normally, a tutor should check individually the activity of each pupil, but this is not possible because of the time restrictions. In this context learning objects and more specifically auto-generative learning objects complements the tutors by: i) proposing exercises to pupils;ii) grading automatically the pupil answers;and iii) offering feedbacks on the given answers. In this sense, we applied the proposed approach on a group of 8th grade, 50 pupils, at the Arithmetic disciplines focusing on intervals and inequations. We created nine auto-generative learning objects where reunion, intersection, the difference of intervals was exercised. We modeled learning objects for five types of inequations. The study shows that the proposed method's effectiveness is very close to the classic evaluation method. We collected the opinions of pupils in a satisfaction questionnaire about using auto-generative learning objects and the results state that 92% of pupils liked them. © 2021, National Defence University - Carol I Printing House. All rights reserved.
ABSTRACT
Educational process suffers during pandemics from multiple causes. Pupils are switched from face to face classes to online classes. Thus, the communication between the pupil and tutor is more restricted. The tutor will not get the same feedback from the pupil and the pupil will not get the same attention from the tutor as in the classroom. Usually, pupils do not turn on their cameras, and tutors are not allowed to enforce them in this sense. Pupils become more autonomous in the learning process. In this context, the use of auto-generative learning objects could assist the tutor since they can generate exercises to be solved by the pupil and also give feedback. © 2021 IEEE.
ABSTRACT
Exams in pandemic suffer from the impossibility of the tutor to properly supervise the students. All the exams are given online where students have lots of inspiration sources. As an effect, the student grades increased allowing them higher scholarships that normally would not get and other social inequities. To stop these phenomena one way is to create different exam subjects for each student and to give students a limited amount of time to solve them. This kind of approach is tedious and it is not feasible when the student cohort is large. One solution is to abstract subject templates from the concrete subject and then use generator programs based on random numbers to instantiate exam subjects for each student. © 2021 IEEE.