Mobile learning has been increased in past years and has attracted the interests of academicians and educators in the past many years especially in higher education. The mobile-based online test is the buzzing in the current pandemic time. Institutions need to use online learning as a powerful tool for conducting exams and assess the students effectively. Integrating technology in education can be advantageous for universities and help engage better results for students. Therefore, it is important to understand each student their capacities and create a different test based on the required difficulty. Students should be graded based on their capabilities. The purpose of the research study is to develop the progressive model with the calibration of difficulty level according to the student capacity. To achieve the goal, a test of 20 python questions was conducted on 120 students with each question having difficulty given by 8 field experts. To verify the model, 5 categories were formed with different difficulty levels which in turn gave satisfactory results. To find a relation between the initial difficulty and the calculative difficulty based on the student response, a correlation test was conducted. After careful analysis of the question difficulty and student responses, it was observed that both are highly dependent on each other wherein the difficulty level of any question can be calculated using incorrect answers. The correlation coefficient obtained between them was 0.9833. Upon collecting the difficulty of the questions and student responses, respective grading could be done using the stated formula. Later on, the progressive model was simulated with five different cases (Best case, above-average case, below average case, the average case, worst case). The model outperformed in all the cases with appropriate difficulty levels. Online Tests have ushered a revolution in the assessment of students but yet they tend to be unpopular in India as the evaluation based on pen-paper approach is preferred. The main reasons for this are difficult to grade everyone at the same level, susceptible to cheating, and transition to open books. Using our study, universities can identify obstacles, and prepare an appropriate result-driven plan of action for implementing the mobile-based online test and make easy migration from paper-based test to online test.

移动学习在过去几年中有所增加，并在过去多年中引起了院士和教育工作者的兴趣，尤其是在高等教育领域。基于移动的在线测试是当前大流行时期的嗡嗡声。机构需要使用在线学习作为进行考试和有效评估学生的有力工具。将技术整合到教育中可能对大学有利，并有助于为学生取得更好的成绩。因此，了解每个学生的能力并根据所需的难度创建不同的测试非常重要。应根据学生的能力对学生进行评分。研究的目的是根据学生的能力开发具有难度级别校准的渐进模型。为了实现目标，对 120 名学生进行了 20 道 Python 问题的测试，每个问题都有 8 位领域专家给出的难度。为了验证模型，形成了 5 个不同难度级别的类别，从而得到了令人满意的结果。为了根据学生的反应找出初始难度和计算难度之间的关系，进行了相关性测试。在仔细分析了问题的难度和学生的回答后，发现两者都高度依赖于彼此，其中任何问题的难度级别都可以使用错误的答案来计算。它们之间得到的相关系数为0.9833。在收集问题的难度和学生的回答后，可以使用规定的公式进行相应的评分。稍后的，渐进模型用五种不同的情况（最佳情况、高于平均水平的情况、低于平均水平的情况、平均情况、最坏的情况）进行了模拟。该模型在所有具有适当难度级别的情况下都表现出色。在线测试在学生评估方面引发了一场革命，但它们在印度往往不受欢迎，因为基于笔纸方法的评估更受欢迎。造成这种情况的主要原因是难以对每个人进行同一级别的评分，容易作弊，以及向开放书籍过渡。通过我们的研究，大学可以识别障碍，并制定适当的以结果为导向的行动计划来实施基于移动的在线测试，并轻松地从纸质测试迁移到在线测试。该模型在所有具有适当难度级别的情况下都表现出色。在线测试在学生评估方面引发了一场革命，但它们在印度往往不受欢迎，因为基于笔纸方法的评估更受欢迎。造成这种情况的主要原因是难以对每个人进行同一级别的评分，容易作弊，以及向开放书籍过渡。通过我们的研究，大学可以识别障碍，并制定适当的以结果为导向的行动计划来实施基于移动的在线测试，并轻松地从纸质测试迁移到在线测试。该模型在所有具有适当难度级别的情况下都表现出色。在线测试在学生评估方面引发了一场革命，但它们在印度往往不受欢迎，因为基于笔纸方法的评估更受欢迎。造成这种情况的主要原因是难以对每个人进行同一级别的评分，容易作弊，以及向开放书籍过渡。通过我们的研究，大学可以识别障碍，并制定适当的以结果为导向的行动计划来实施基于移动的在线测试，并轻松地从纸质测试迁移到在线测试。造成这种情况的主要原因是难以对每个人进行同一级别的评分，容易作弊，以及向开放书籍过渡。通过我们的研究，大学可以识别障碍，并制定适当的以结果为导向的行动计划来实施基于移动的在线测试，并轻松地从纸质测试迁移到在线测试。造成这种情况的主要原因是难以对每个人进行同一级别的评分，容易作弊，以及向开放书籍过渡。通过我们的研究，大学可以识别障碍，并制定适当的以结果为导向的行动计划来实施基于移动的在线测试，并轻松地从纸质测试迁移到在线测试。