In statistical hypothesis testing, a Type II error is a situation wherein a hypothesis test fails to reject the null hypothesis that is false. In other words, it causes the user to erroneously not reject the false null hypothesis because the test lacks the statistical power to detect sufficient evidence for the alternative hypothesis. As the sample size increases, the probability of a Type II error (given a false null hypothesis) decreases, but the maximum probability of a Type I error (given a true null hypothesis) remains alpha by definition. This is because a larger sample size increases the chances to capture the differences in the statistical tests, as well as raises the power of a test.
further specified an important element that students have a general preference for face-to-face classroom interaction with peers and professors and when offered a choice between live-streamed lectures or face to face classes, students opted to go to in face classes and live-streamed lectures only mildly decrease the attendance of in face classes. This is reinforced by the findings of Weil et al (2014) wherein students still did not want to forego the opportunities of being in class with peers and faculty. Even though they value online activities, having it experienced in actual is still favored for the students.
Making remote learning work for all students, according to Armstrong and Mensah, is difficult. The best tools can be in place, but without equitable access by all students to the tools, adequate preparation time and training for faculty, and the adoption of existing curricula, or the development of brand-new course syllabi, it will be difficult to replicate the in-person learning experience, online. Studies have further revealed the complexity of distance education, highlighting the numerous variables involved in any educational setting, not to mention other factors affecting the field, such as social, economic, and geopolitical challenges (Saba, 2000). Complexity leads to different adjustments needed to be made from both students and professors.
Al-Adwan (2018) stated that students’ perceptions of online learning are influenced by a great deal of factors with the “relative advantage, complexity, social influence, perceived enjoyment and the self-management of learning” coming out on top. Al-Adwan further remarked that if the online learning environment is appropriately constructed to have a beneficial impact on students’ academic performance, they will be more inclined to fully integrate themselves into the online learning environment. On another note, online learning environment developers should line their vision and objectives when developing on designing meaningful and customized learning applications to address point per point the student’s academic and non-academic requirements which in turn, results in an increase of performance across the horizon.
Moreover, Ali (2020) highlighted the importance of staff readiness, confidence, student accessibility, and motivation play in online distance learning. For successful assimilation of Higher E
