Abstract:

Intelligent tutorial online learning environments (IL) support learners with, among other things, step-by-step feedback for individual solution steps, optionally retrievable content-related and heuristic assistance, and error feedback [1], whereas passive online learning environments (PL) at best provide feedback on the correctness of results.

For a comparative study of the effects of an IL with a PL on learning gains, intrinsic motivation and acceptance of the learning environments, two such learning environments, comparable in content and learning scope, were developed for translating numbers between different place value systems. This subject learning content was chosen as an established part of university teacher education and at the same time because of the often observed mathematical difficulties for some of the students.

In an experimental online study, a sample of student teachers (N = 93, elementary or secondary mathematics) in southern Germany was randomly assigned to IL or PL. Before using the learning environment, participants were asked about their mathematical self-concept (MSC) with an online questionnaire. After use, the acceptance based on the Technology Acceptance Model (TAM) [2] and the intrinsic motivation (IM) during use was recorded with further items. In addition, the effects of the learning environment on learning success were determined with a pre-post-test.

The use of both learning environments was characterized by large learning gains (IL d = .922, PL d = .840), but without significant differences between IL and PL in terms of learning success and acceptance. A descriptive, small difference in motivation in favor of IL also narrowly failed to reach significance. Independent of the learning environment, a significant effect on learning growth was confirmed for MSC (p = .020, part. Eta-Quadrat = .059) and, in a second model, IM's Pressure/Suspense scale (p = .038, part. Eta squared = .047) when IM's Interest/Enjoyment scale (p = .157, part. Eta squared = .022) was included simultaneously as a covariate.

The paper presents the learning environments with their learning theory backgrounds [3] and the statistical results of the study. The contribution of the findings to the state of research is discussed with reference to the experimental situation of the online study, to the duration of the actual usage time, and with regard to the relevance of the learning object for the participants.

References:
[1] J. R. Anderson, A. T. Corbett, K. R. Koedinger, and R. Pelletier, “Cognitive Tutors: Lessons Learned,” Journal of the Learning Sciences, vol. 4, no. 2, pp. 167–207, 1995, doi: 10.1207/s15327809jls0402_2.
[2] V. Venkatesh and F. D. Davis, “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” Management Science, vol. 46, no. 2, pp. 186–204, 2000, doi: 10.1287/mnsc.46.2.186.11926.
[3] S. Narciss, “Feedbackstrategien für interaktive Lernaufgaben,” in Handbuch Bildungstechnologie, (H. Niegemann and A. Weinberger, eds.), pp. 369–392, Berlin, Heidelberg: Springer Berlin Heidelberg, 2020.

Keywords:

intelligent cognitive tutor, online learning, feedback, translation between place value systems, Technology Acceptance Model