Abstract
First-year science majors at The University of Hong Kong have different levels of proficiency in mathematics, with a significant proportion lacking the necessary calculus background for a compulsory freshman science foundation course. A supplementary calculus e-learning platform was implemented so that students lacking the prerequisite could gain the necessary knowledge and skills at their own pace. This chapter presents quantitative and qualitative analyses of the learning analytics, including the behavior as well as the achievements of the users. Pretest and posttest results are used to assess the effectiveness of the platform. Questionnaires completed by the users are utilized to explore aspects for improvement. We hope this study can stimulate discussions on the assessment of e-learning, as well as shed light on the factors contributing to the efficiency and effectiveness of similar platforms.
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Notes
- 1.
The t-test allowing for unequal variance is used as an F-test comparing the variances of pretest scores of the two groups suggests that we cannot treat the variances as the same at 1% significance level.
- 2.
Due to limitations in the user log, we define “watching a video” as clicking the video link. The assumption is that users watch the video once every time they click the link. Unfortunately, this cannot capture the case where a user stays on the same page and watch a video over and over again.
- 3.
For example, if a user had clicked on the link of one particular video n times throughout the semester, his/her qvideo would be n while cvideo would only be 1.
- 4.
Average frequency refers to the number of times a student watched the same video or submitted the same quiz. For instance, if a student has watched 10 different videos (cvideo = 10) with a total view count of 30 (qvideo = 30), then his/her average frequency of watching videos is defined as qvideo/cvideo = 3.
- 5.
As for column (1) of Table 5.4, the same conclusion can still be reached if the independent variable is students’ self-reported calculus background (rBackground) instead of their pretest score.
- 6.
Originally, we also wanted to analyze whether and how students’ prior knowledge affects the substitute/complement relationship between the videos and the quizzes. Hence, the interaction effect between cvideo and pretest was initially included in the full model. At the significance level of 5%, the interaction effect was not statistically significant. In other words, the relationship between quizzes and videos is not affected by a student’s calculus knowledge background. In columns (8) and (9), therefore, only the main effects of cvideo and pretest are included.
- 7.
For instance, students may have managed to find practice questions from other sources.
- 8.
Defined as assigning number 3, 4, 5 to the statement “the quizzes aligned closely with the content of the videos” on a Likert Scale of 1–5: 1 means “strongly disagree” and 5 means “strongly agree”.
- 9.
Only 16 and 14% of the students respectively disagree or strongly disagree with the statement that “Compared with the midterm/final examination, the quizzes were too easy”.
- 10.
For instance, in the course dimension part of the survey, an optional question asked “What gave you a better learning experience than the e-learning platform in Calculus learning, if any?”.
- 11.
In the quiz part of the survey, a question asked if there is “any other reasons that stopped/prevented you (the students) from working on the quizzes”. A number of respondents claim the quizzes are time-consuming or they do not have enough time for revision.
- 12.
The average is defined as the total number of videos/quizzes accessed every day divided by the total number of students with certain mathematical background. 71 students reported themselves as having no prior calculus background, and 154 students reported they have.
- 13.
This is observed in students’ response to the question “I would suggest the following changes for improvement:”. In addition, 88.5% of the respondents agreed with the proposal that “the instructors should suggest a timeline that we finish certain modules of the e-learning platform”.
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We would like to acknowledge here the efforts of other members of the SCNC1111 teaching team and the e-learning platform development team who are not on the author list.
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Liang, L., Yeung, K., Lui, R.K.W., Cheung, W.M.Y., Lam, K.F. (2018). Lessons Learned from a Calculus E-Learning System for First-Year University Students with Diverse Mathematics Backgrounds. In: Silverman, J., Hoyos, V. (eds) Distance Learning, E-Learning and Blended Learning in Mathematics Education. ICME-13 Monographs. Springer, Cham. https://doi.org/10.1007/978-3-319-90790-1_5
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