Student’s effort and motivational factors behind it have an essential role in determing how students approach new tasks and perform in them (e.g., Kupiainen et al., 2014). Together, they affect the ability to apply the cognitive processes fundamental to identifying problems and designing and applying solutions (Kong & Abelson, 2019; Skinner ym., 1998). These processes have traditionally been measured and evaluated through self-reports and observation. While these methods undoubtedly have an important place in the human sciences, they have challenges regarding validity and large sample sizes. One solution to these challenges is that the technology's vast potential allows seamless data collection from individuals in digital environments without disrupting their natural activities (Wise & Gao, 2017). Hence, this paper focuses on investigating what time on task, number of trials, and use of problem-solving strategies in different tasks tell us about student performance and whether the results in different tasks are consistent with each other. The relations between these task behavior indicators are examined from the perspective of motivational profiles students may hold by examining whether the profiles differ in this matter.

In this study, the focus is on students' control-related beliefs within the framework of Action-Control Theory (Skinner et al., 1988). According to the theory, perceived control encompasses beliefs about the relation of agents, means, and ends, shaping a student's perception of how school outcomes are achieved and the extent to which they are actively involved. These beliefs are found to be related to school achievement in to a varying degree and varying hindering or fostering effects. Accordingly, while some students with beliefs that have shown to positively predict school performance have done well, other students with similarly above average beliefs have done less well, highlighting the existence and importance of different combinations of beliefs when considering their association with motivational orientation and performance (Malmberg & Little, 2007).

Treating time use as a measure of motivational investment in a task is grounded in Carroll's Model of School Learning (Carroll, 1989). According to the model, students vary in the time they need to learn, which in turn depends on students' aptitude for the task, their ability to understand instruction, and the quality of instruction. Higher aptitude corresponds to shorter learning times, while lower aptitude may require more effort. The time students ultimately invest in learning is composed of the time allocated for learning and the time students are willing to dedicate. The required time, the time spent, and the quality of instruction act as the determinants of the level of learning (Kupiainen et al., 2014). Computer-based assessment (CBA) research has confirmed that students' too short time on task indicates a lack of effort and task commitment (e.g., Wise & Gao, 2017). This results from reacting too quickly compared to the time needed for a proper task solution (Schnipke, 1995). This supports findings in problem-solving tasks, indicating that in every ability level longer response times positively correlate with correct answers as task difficulty increases (Goldhammer et al., 2014).

The study delves into the diverse strategies individuals employ during problem-solving that guide the problem-solving process and ultimately influence how effectively they navigate problem-solving situations (Stubbart & Ramaprasad, 1990). Some problems may require multiple trials and inductive reasoning, while in other problems the most appropriate way is to test how individual variables affect the outcome, isolating the effect of other variables. CBA enables the exploration of these strategies by utilizing log data collected during tasks, which have been done in the past, particularly for studying the differentiation of the effect of variables in solving more complex problems (e.g., Greiff et al., 2016).

This study uses national longitudinal data for the academic year 2021-2022 (N = 8556) collected by the University of Tampere and the University of Helsinki in the framework of the DigiVOO project. This study does not use the longitudinal aspect but includes measures from three different measurement points. Motivational beliefs were assessed using Action-Control Theory Scales (e.g., Chapman et al., 1990), covering agency beliefs on ability and effort, control expectancy, and means-ends beliefs on various factors. Each scale included three items with a 7-point Likert-type scale (1 = not true at all, to 7 = very true). The success rate in problem-solving tasks was computed from the overall percentage of correct answers in programming tasks (code building and debugging) and a task measuring vary-one-thing-at-a-time (VOTAT) problem-solving strategies (Greiff et al., 2016). The programming tasks involved coding a robot to pick up a sock in a room with obstacles. The VOTAT-based task, Lilakki, required students to vary conditions for optimal plant growth. Task behavior indicators were derived from log data, including time on task measured in seconds and trials related to the number of completed items in programming tasks. Problem-solving strategies (VOTAT) in Lilakki were analyzed by calculating the relative percentage of used strategies from the overall number of trials in the task. General Point Average (GPA) reflected students' prior ability against the achievement in problem-solving tasks, incorporating grades in Finnish, mathematics, English, history, and chemistry. In this study, latent profile analysis (LPA) and multigroup structural equation modeling (SEM) will be conducted. LPA is used to identify subgroups of students based on their self-reports on the motivational measures. Fit indices for LPA are Bayesian Information Criterion (BIC), sample size adjusted BIC (SABIC), Akaike Information Criterion (AIC), Consistent Akaike Information Criterion (CAIC), Vuong-Lo-Mendel-Rubin likelihood ratio test (VLMR), adjusted VLMR, and Bootstrap Loglikelihood ratio test (BLRT) and entropy. In addition, the elbow plot method for AIC, CAIC, BIC, and SABIC is used, and the qualitative investigation is done against substantive theory and previous studies. In multigroup SEM, the MLR estimator will be used. The goodness of fit of the model will be assessed by the following fit indices: RMSEA (< 0.05 = good model, < 0.08 = acceptable model) and CFI & TLI (> 0.95 = good model, > 0.90 = acceptable model).

Preliminary results concerning motivational profiles have been analyzed. Based on the fit indices, elbow method, and qualitative inspection, a 5-class solution in LPA was considered the best fit. The five motivational profiles are preliminarily named Avoidant, Normative, Mildly Agentic, Agentic, and Mixed. Students in the Agentic (Class 1) profile saw their effort and ability and control over school achievement most positively compared to believing that luck and ability would determine school outcomes. Thus, this profile was considered to have the most adaptive beliefs. Mildly agentic (Class 2) and Moderate (Class 3) reflected pattern demonstrated by Agentic students but moderately. Avoidant (Class 4) students had the lowest adaptive beliefs (i.e., beliefs about their ability, effort, and control as well as effort as a means for success) and attributed school outcomes to ability over other beliefs. In the Mixed profile (Class 5), students had one of the most positive adaptive beliefs with the Agentic profile. Similarly, they possessed the most positive means-ends beliefs on ability and luck. This profile is seen to indicate adaptive as well as maladaptive consequences to achievement (Malmberg & Little, 2007). In multigroup SEM, the hypothesis is that motivational profiles play a role in how task behavior indicators (time on task, trials and strategies), prior ability, and performance in problem-solving tasks are related to each other due to differences in their approaches to novel tasks (see Callan, et al., 2021; Skinner et al., 1998). In summary, this paper delves into the complex dynamics of effort, motivation, and cognitive processes during academic tasks, utilizing innovative technology for data collection. The findings provide novel insights into students' problem-solving strategies.

Callan, G. L., Rubenstein, L. D., Ridgley, L. M., Neumeister, K. S., & Finch, M. E. H. (2021). Selfregulated learning as a cyclical process and predictor of creative problem-solving. Educational Psychology, 1–21. https://doi.org/10.1080/01443410.2021.1913575 Carroll, J. B. (1989). The Carroll model: A 25-year retrospective and prospective view. Educational Researcher, 18, 26–31. https://doi.org/10.3102/0013189X018001026 Chapman, M., Skinner, E. A., & Baltes, P. B. (1990). Interpreting correlations between children’s perceived control and cognitive performance: Control, agency or means–ends beliefs. Developmental Psychology, 26, 246–253. https://doi.org/10.1037/0012-1649.26.2.246 Goldhammer, F., Naumann, J., Stelter, A., Klieme, E., Toth, K. & Roelke, H. (2014). The time on task effect in reading and problem solving is moderated by task difficulty and skill: Insights from a computerbased large-scale assessment. Journal of Educational Psychology, 106(3), 608–626. https://doi.org/10.1037/a0034716 Greiff, S., Niepel, C., Scherer, R., & Martin, R. (2016). Understanding students' performance in a computer based assessment of complex problem solving. An analysis of behavioral data from computer-generated log files. Computers in Human Behavior, 61, 36–46. https://doi.org/10.1016/j.chb.2016.02.095 Kong, S.-C. & Abelson, H. (2019). Computational Thinking Education. Springer Singapore. https://doi.org/10.1007/978-981-13-6528-7 Malmberg, L.-E., & Little, T. D. (2007). Profiles of ability, effort, and difficulty: Relationships with worldviews, motivation and adjustment. Learning and Instruction, 17(6), 739–754. https://doi.org/10.1016/j.learninstruc.2007.09.014 Schnipke, D. L. (1995). Assessing speededness in computer-based tests using item response times. [Dissertation, John Hopkins University]. The Johns Hopkins University ProQuest Dissertations Publishing. Skinner, E. A., Chapman, M. & Baltes, P. B. (1988). Control, means-ends, and agency beliefs: A new conceptualization and its measurement during childhood. Journal of Personality and Social Psychology, 54, 117–133. https://doi.org/10.1037/0022-3514.54.1.117 Skinner, E. A., Zimmer-Gembeck, M. J. & Connell, J. P. (1998). Individual differences and the development of perceived control. Monographs of the Society for Research in Child Development, 6(2–3), 1–220. https://doi.org/10.2307/1166220 Stubbart, C. I., & Ramaprasad, A. (1990). Conclusion: The evolution of strategic thinking. Teoksessa A. Huff (toim.), Mapping strategic thought. John Wiley and Sons. Wise, S. L., & Gao, L. (2017). A general approach to measuring test-taking effort on computer-based tests. Applied Measurement in Education, 30(4), 343–354. https://doi.org/10.1080/08957347.2017.1353992