'Achievement Goals, e-Learning Behavior, and Exam Performance' (AsPredicted #94899)
Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has 2 authors.
Pre-registered on 04/22/2022 06:00 AM (PT)
1) Have any data been collected for this study already? No, no data have been collected for this study yet.
2) What's the main question being asked or hypothesis being tested in this study? RG 1: Our main research goal is to investigate associations between learning behavior in an e-learning environment and performance in graded exams. We propose:
H1a: Higher total learning time (TLT) predicts higher exam performance (EP).
H1b: More distributed learning (DL)compared to massed learning predicts higher EP.
H1c: Lower degrees of procrastination (DoP) predict higher EP.
RG 2: We also aim to investigate to which degree achievement motivation (here: achievement goals) explains both e-learning behavior and exam performance:
H2a: Stronger learning approach goals and task approach goals predict higher TLT, more DL, lower DoP and higher EP.
H2b: Stronger normative approach and appearance approach goals predict less DL and higher EP.
H2c: Stronger normative avoidance and appearance avoidance goals predict less DL, higher DoP and lower EP.
H2d: Stronger work avoidance goals predict lower total learning time, less DL, higher DoP and lower EP.
We also explore associations for task avoidance goals.
RG 3: We investigate whether the measured set of achievement goals explains incremental variance on EP beyond the impact of the e-learning parameters (H3a) and vice versa (H3b).
RG 4: We investigate whether the depicted parameters mediate associations between achievement goals and EP. In this regard, we expect:
H4a: TLT, DL and fewer DoP positively mediate the association between task approach goals as well as learning approach goals and EP.
H4b: DL and DoP mediate the association between normative avoidance as well as appearance avoidance goals and EP.
H4c: TLT, DL and fewer DoP negatively mediate the association between work avoidance goals and EP.
We also explore whether the observed parameters mediate the association between EP and normative approach goals as well as appearance approach goals.
RG 5: Additionally, we investigate whether the associations between achievement goals and e-learning behavior are moderated by beliefs about the utility of the learning software for goal pursuit.
H5: We assume that the association between all measured achievement goals and e-learning behavior (indicated by TLT, DL and DoP) is moderated by students' beliefs about the utility value of engagement in e-learning for the pursuit of the respective goal. We expect those utility beliefs to bolster positive associations and diminish negative ones.
3) Describe the key dependent variable(s) specifying how they will be measured. Learning behavior: We will use the log files provided by the e-learning software to conduct the respective parameters as follows:
• Overall learning time will be measured as the total time that participants engage in learning activities with the software after acquiring the software until the exam.
• Massed learning will be the variance of learning activities over time. We will aggregate learning time per week and compute the variance over those learning times per week for every participant. Higher variance indicates more massed learning, while more distributed learning will lead to lower variance.
• Degree of procrastination will be computed by the weeks left to the exam when participants reach 50% of their cumulated individual learning activities. Less time left till exam at the thresholds of 50% cumulated learning activities indicates higher degrees of procrastination.
Exam performance: We will ask for permission to match participants' exam performance after the exams are written in the questionnaire. We will use students' identification number to match exam data to the logfiles and survey data regarding achievement goals.
4) How many and which conditions will participants be assigned to? There is only one general condition and we will not conduct experimental manipulations.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. We will run (hierarchical) regression analyses and (moderated) mediation models to evaluate our research questions.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. We will check the data for outliers (exceptionally high amount of learning time and exceptionally low amount of learning time) as well as behavioral patterns that indicate that the software was not used properly (e.g., some learning activity after purchase followed by little activity over time). To test the robustness of our effects, we will explore whether excluding extreme cases or cases indicating unproper use of the software will affect the results.
We will also conduct further exploratory inspections into the data searching for further parameters that might help us to understand both learning patterns and software use better. These inspections can guide further data exclusion when investigating our main hypotheses.
7) How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about exactly how the number will be determined. The sample size is limited by the number of users that purchase the software in time span between 25.04.2022 and 10.06.2022. We will include all users that purchase the software and who provide informed consent in our sample.
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) We will assess participants' achievement goals, namely mastery approach, task approach and avoidance, normative approach and avoidance, appearance approach and avoidance, and work avoidance, using the respective subscales of the questionnaire by Daumiller (2019). We will only use 3 instead of 4 items per subscale. Based on these original items, we also developed items measuring participants' beliefs about the utility value of the e-learning software for the pursuit of all eight measured achievement goals (3 items per subscale).
Parallel to this study, another research project is evaluating certain features of the e-learning tool, also in dependency of individuals' achievement goals. The respective scales will be collected together with ours in the same questionnaire.
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