Self-regulation of resources in higher education
Strategic learning behaviour mediates the effect of learning strategy knowledge on academic performance
Abstract
Abstract: Resource-management learning strategies are particularly important for performance in higher education. Despite this consideration, the current state of research still lacks evidence on the precise interplay of the different learning process components that affect academic performance. The aim of the present study was to test a mediation model derived from process theories of self-regulated learning, in which students' knowledge about resource-management strategies impacts their academic performance via its behavioural manifestations. N = 106 university students took part in an online course over the period of one semester. Students' resource-management strategy knowledge was assessed at the beginning of the semester, and their use of resource-management strategies was observed via log data of their learning behaviour during the semester while learning through the online course. Academic performance was assessed on the basis of an exam at the end of the semester. The mediation model was tested for three different resource-management strategies: time management, effort regulation, and help seeking. There was a significant indirect effect of strategy knowledge via strategy use on academic performance for all learning strategies considered. We conclude that students' strategic learning behaviour is an indispensable link in the relationship between their strategy knowledge and academic performance, and discuss implications for research and training practice.
Zusammenfassung: Ressourcenmanagementstrategien gelten im Hochschulkontext als besonders relevant für Studienleistungen. Trotz ihrer großen Relevanz bietet der aktuelle Forschungsstand bisher jedoch nur eingeschränkte Evidenz zum Zusammenhang zwischen den verschiedenen Lernprozesskomponenten, die sich im Verlauf des Lernprozesses auf den Lernerfolg von Studierenden auswirken. Das Ziel dieser Studie war es deswegen, ein von etablierten Prozessmodellen zum selbstregulierten Lernen abgeleitetes Mediationsmodell zu testen, in dem der Einfluss des Strategiewissens von Lernenden auf ihre Studienleistung über ihr Lernverhalten vermittelt wird. N = 106 Universitätsstudierende haben über die Dauer eines Semesters an einem Online-Kurs teilgenommen. Das Strategiewissen der Studierenden wurde zu Beginn des Semesters erfasst und ihre Anwendung der entsprechenden Strategien auf Basis von Logdaten beobachtet, die während der Kursbearbeitung im Verlauf des Semesters gesammelt wurden. Die Studienleistung der Studierenden wurde anhand einer Klausur am Ende des Semesters beurteilt. Das postulierte Mediationsmodell wurde für drei verschiedene Ressourcenmanagementstrategien getestet: Zeitmanagement, Anstrengungsregulation, Hilfesuchen. Es zeigte sich ein signifikanter indirekter Effekt des Strategiewissens der Studierenden über ihre Strategieanwendung auf ihre Studienleistungen für alle untersuchten Lernstrategien. Wir kommen zu dem Schluss, dass das strategische Lernverhalten der Studierenden ein elementares Bindeglied zwischen ihrem Strategiewissen und ihrer akademischen Leistung ist. Implikationen für Forschung und Interventionsdesigns werden diskutiert.
Introduction
Theories on the process of self-regulated learning suggest a successive sequence of related knowledge and behaviour components that are necessary to successfully promote learning performance (e.g., Dresel et al., 2015; Schmitz & Wiese, 2006; Zimmerman & Moylan, 2009). It is postulated that suitable knowledge about learning strategies needs to be transferred into situationally appropriate learning behaviour which, in turn, can positively impact learning outcomes. The assumed relations between these learning process components implicitly point to a mediation model in which learning strategy knowledge affects performance via its behavioural manifestation: i.e., the use of the corresponding learning strategies (Pintrich, 2004). This theoretical mediation assumption is empirically supported by evidence demonstrating significant bivariate correlations between strategy knowledge, strategy use, and performance (e.g., Broadbent & Poon, 2015; Donker et al., 2014; Schneider & Preckl, 2017; Waldeyer et al., 2020). However, these relations have mostly been analysed separately, and the corresponding pattern of relations is rarely brought together into one model. Specifically, some studies focus on the relation between learning strategy knowledge and performance without explicitly considering their behavioural manifestation (e.g., Donker et al., 2014; Maag Merki et al., 2013), and a majority of studies tend to focus on self-reports of behavioural manifestations without taking into account the underlying strategy knowledge (e.g., Roth et al., 2016; Rovers et al., 2019). Without including all relevant interrelations, the importance of each process component may be over- or underestimated in relation to its impact on successful (self-regulated) learning. This, in turn, could lead to bias when it comes to practical implications on the basis of corresponding findings – e.g., in the form of learning strategy trainings (e.g., de Boer et al., 2018; Donker et al., 2014; Jansen et al., 2019; Trentepohl et al., 2022). Given this risk, it appears highly important to gather further evidence on the interrelations between the underlying process components.
The aim of the present study was to test the mediating influence of the use of learning strategies on the relationship between learning strategy knowledge and academic performance in a sample of first-year university students. Thereby we contribute to empirically validating theoretical postulated models on the self-regulated learning process in an all-encompassing manner. Moreover, we based our data collection on behavioural data of students' actual strategy use, instead of self-reported data, to further contribute to the state of research by using an objective and valid measure of self-regulated learning (Azevedo & Gasevic, 2019; Rovers et al., 2019). Since our sample comprised first-year students, we decided to focus on the category of resource-management learning strategies for this study, due to its particular relevance for coping with changes during the period of transition from school to university, such as the less-predetermined structure of the learning environment and increased task complexity (Broadbent & Poon, 2015; Dent & Koenka, 2016; Gibney et al., 2011).
Theoretical background
The process of self-regulated learning is described as a cyclical sequence of consecutive phases that can successively promote learning outcomes (e.g., Pintrich, 2000, 2004; Schmitz & Wiese, 2006; Winne & Hadwin, 1998; Zimmerman & Moylan, 2009). These phases usually refer to the preparation, execution, and evaluation of learning activities (see also, forethought, performance, and self-reflection phase, Zimmerman & Moylan, 2009; pre-action, action, and post-action phase, Dresel et al., 2015). The first phase basically comprises goal setting and strategic planning. Here, knowledge about different learning strategies and the conditions for their effective use is essential. In the second phase, the available strategy knowledge needs to be transferred into concrete learning behaviour. This learning behaviour refers to the use of previously selected learning strategies and self-control during performance. The third phase refers to the evaluation of recent learning performance. After the learning performance itself, this evaluation phase serves to optimise future learning processes by reflecting both on the executed learning strategies and on learning performance, to make causal attributions and adapt existing knowledge on relevant learning strategies.
Consequently, referring to the pre-action and action phases, which are deemed particularly relevant by experts for almost every learning situation (Dresel et al., 2015), the process of applying self-regulated learning strategies can roughly be subdivided into a knowledge component and an action component (e.g., Schmitz & Wiese, 2006; Winne & Hadwin, 1998; Zimmerman & Moylan, 2009). The knowledge component describes the availability of learning strategies, enabling learners to spontaneously choose potentially helpful strategies in specific learning situations. The action component addresses the ability to transfer the available strategy knowledge into contextually appropriate learning behaviour. To make effective use of their strategy repertoire, learners consequently need to transfer available knowledge about learning strategies into contextually appropriate learning behaviour, which in turn has a positive impact on learning outcomes (i.e., performance). In the following, these process components are referred to as strategy knowledge and strategy use respectively.
Overall, self-regulated learning processes are composed of many different and contextually varying aspects, just as different learning situations typically make different demands regarding learners' self-regulation (Ben-Eliyahu & Bernacki, 2015; Panadero, 2017; Virtanen & Nevgi, 2010). Accordingly, there are different types of learning strategies that learners need to know and select in the preparation phase, and use in the execution phase, to improve learning outcomes. These types of learning strategies include cognitive, metacognitive, and resource-management strategies (Dresel et al., 2015; Weinstein & Hume, 1998; Winne & Hadwin, 1998). Cognitive strategies refer to sets of mental processes that serve the reception, processing, and storage of information (e.g., memorising, elaborating). Metacognitive strategies are superordinate to cognitive strategies and refer to mental processes aiming at the conscious control of learning processes (e.g., monitoring, regulation). Both cognitive and metacognitive learning strategies refer to the regulation of the learning process and help students to understand content, whereas resource-management strategies help to support learning processes by developing individual learning conditions. Resource-management strategies in turn include strategies that support the management of one's own learning activities using internal resources (e.g., time management, effort regulation), and strategies that focus primarily on the use of external sources of information to support learning processes (e.g., help seeking, peer learning; Pintrich et al., 1991). Since resource-management strategies are particularly important during the transition from high-school to university learning contexts (e.g., Dresel et al., 2015; Gibney et al., 2011), the present study focuses primarily on resource-management strategies.
Strategy knowledge, strategy use, and academic performance
The theoretically postulated relations between students' learning strategy knowledge, strategy use, and academic performance are supported by evidence demonstrating significant bivariate correlations in the context of all three categories of learning strategy (i.e., cognitive, metacognitive, and resource management). First, students' strategy knowledge should be positively related to their strategy use (Borkowski et al., 2000; Zimmerman & Moylan, 2009). Indeed, previous studies including tests on students' learning strategy knowledge and self-reports on their corresponding strategy use found significant correlations between these variables (e.g., Karlen & Compagnoni, 2017; Maag Merki et al., 2013; Steuer et al., 2019; Young & Fry, 2008). Thillmann and colleagues (2013) even found metacognitive strategy knowledge to be significantly correlated with students' use of metacognitive strategies (i.e., regulation) as measured by log data in a computer-based learning environment, providing strong support for the assumed connection between students' knowledge about a learning strategy and their use of that same strategy.
In a second step, students' use of learning strategies should be positively related to their academic performance. Here, significant correlations have been found between students' self-reported use of different learning strategies and measures of academic performance (e.g., Broadbent & Poon, 2015; Crede & Phillips, 2011; Fang, 2014; Nabizadeh et al., 2019; Young & Fry, 2008), as well as between protocols of students' actual use of cognitive and metacognitive learning strategies and performance (e.g., Berthold et al., 2007; Nückles et al., 2009; 2020). Furthermore, intervention studies that aimed at improving students' active use of self-regulated learning strategies showed significant effects on academic performance (for an overview, see Jansen et al., 2019). With special regard to resource-management strategies, Boerner and colleagues (2005) as well as Waldeyer and colleagues (2020; 2022) found positive correlations between the use of most resource-management strategies with academic performance. Other studies reported similar results, with correlations between academic performance and the resource-management strategies time-management, effort-regulation, and, to a smaller extent, help-seeking strategy use, being particularly noteworthy (Broadbent & Poon, 2015; Crede & Phillips, 2011; Fong et al., 2023; Khiat, 2019; Schneider & Preckl, 2017).
Finally, significant correlations have been demonstrated for the relation between students' learning strategy knowledge and their academic performance regarding cognitive and metacognitive strategies (e.g., Gul & Shehzad, 2012; Maag Merki et al., 2013; Panchu et al., 2016; Young & Fry, 2008). In the same vein, intervention studies in which learning strategy instructions were provided to increase students' knowledge regarding different types of learning strategies, showed positive effects on academic performance as well (de Boer et al., 2018; Donker et al., 2014; Trentepohl et al., 2022). However, given the successive phase structure of the learning process, as postulated theoretically by process models of self-regulated learning (e.g., Schmitz & Wiese, 2006; Zimmerman & Moylan, 2009), it is assumed that the observed effect of strategy knowledge on academic performance is mediated via students' strategy use. Accordingly, behaviours related to the use of learning strategies have been considered as ‘mediators between personal and contextual characteristics and actual achievement or performance’ in theory before (Pintrich, 2004; p. 388). The aforementioned single-relation findings between students' strategy knowledge, strategy use, and academic performance therefore provide an important initial indication of this mediating relationship which, however, to the best of our knowledge has not yet been tested in an encompassing mediation model for resource-management strategies, nor for any category of learning strategy.
The present study
The aim of the present study was to empirically test the postulated mediating role of resource-management strategy use linking resource-management strategy knowledge and academic performance. Specifically, we addressed the research question whether students' resource-management strategy use mediates the effect of resource-management strategy knowledge on academic performance. We expected that resource-management knowledge would positively predict academic performance, but that this relationship would be mediated via resource-management strategy use. Accordingly, a mediation model was presumed, with resource-management strategy knowledge the predictor variable, resource-management strategy use the mediator variable, and academic performance the criterion variable (see Figure 1).
This assumed mediation model was tested for multiple resource-management strategies. In a comprehensive meta-analysis on psychological correlates of students' academic performance, Richardson and colleagues (2012) reviewed 50 conceptually distinct correlates of university students' grade point average from several research domains. Besides other variables, the investigated correlates comprised several self-regulated learning strategies. Among these, the resource-management strategy categories time management, effort regulation, and help seeking were found to be the strongest significant correlates of university students' grade point average (weighted correlation coefficient r+ = .15 − .32). Schneider and Preckl (2017) came to similar results in their systematic review of variables associated with achievement in higher education, and thus deemed these strategies particularly important for students in higher education. Therefore, the mediation hypothesis was tested with respect to these three resource-management strategy categories.
There are several established self-report instruments that measure students' resource-management strategy use (e.g., MSLQ; Pintrich et al., 1991). However, self-assessments can be biased and inaccurate when students report on their learning behaviour, especially over longer periods of time (for an overview see Paulhus & Vazire, 2007; for self-reports in the context of self-regulated learning see also Rovers et al., 2019; Spörer & Brunstein, 2006; Winne & Jamieson-Noel, 2002; Wirth & Leutner, 2008). Therefore, we based our data collection on a performance test of students' resource-management strategy knowledge, combined with behavioural data of their actual strategy use while learning for a relevant exam. By doing so, our study not only contributes to the body of educational psychology literature by testing the overall mediation model, instead of single relations only, but also uses objective and valid trace data of self-regulated learning behaviour in a real learning context, instead of relying on the self-reported quantity of strategy use, as has tended to be common in previous research (Azevedo & Gasevic, 2019; Paulhus & Vazire, 2007; Roth et al., 2016; Rovers et al., 2019).
Methods
Sample and design
The sample consisted of N = 106 first-year students from the domain of civil engineering at a German university (28.3% female, Mage = 20.8 years, SDage = 3.1 years). The students voluntarily participated in a course that was specifically designed for this study and integrated into the selection of elective modules of their degree programme. The study followed a correlative design with several points of measurement over the period of one semester. Participants gave their written consent for participation, completed an online questionnaire on demographic data and a test on resource-management strategy knowledge at the beginning of the semester, before working independently with an online learning tool within the university's Moodle learning environment. Students' use of resource-management strategies during the semester was assessed using log data of their learning activities while learning with the online learning tool. The course ended with an exam on the online learning tool's content, which served as an indicator of students' academic performance. Participants received €100 as compensation for completing the study, and three credit points for successfully undertaking the course (i.e., passing the exam). This study complied with the human subject guidelines of national research committees, as well as the APA Ethics Code Standards.
Material and measures
Learning material. An online learning tool was designed that allowed the recording of indicators for participants' resource-management strategy use while they solved tasks that required the application of different learning strategies. The online tool consisted of 13 lessons, each on topics that were not related to participants' main study subject (e.g., basic memory models, multimedia learning). The exam-relevant topics were divided into 12 lessons, with a total of 46 practice questions, which allowed students to regularly check their knowledge acquired, whereas the last lesson was a comprehensive exercise they had to submit. The course content was provided exclusively via the online tool which participants were free to work with over the semester. In the course overview menu, students were shown which lessons they had already completed and which were yet pending. There was brief feedback (correct/false answer) on the practice questions, and students were free to repeat any question that they had answered incorrectly (see Figure 2; see also Supplemental Materials for illustrations of learning materials and practice questions). Collected log data included timestamps for every student login and logout, as well as for the beginning and completion of every lesson, section, and practice question. Further, their performance in the practice questions was recorded, as was the frequency of incorrectly answered questions they repeated. The online tool allowed students to re-read instructions and deadlines at any time.
Resource-management strategy knowledge. Participants' strategy knowledge was measured using three subscales of the Resource-Management Inventory (ReMI; Waldeyer et al., 2020). For our data analysis we used the included knowledge tests regarding time management (7 items), effort regulation (8 items), and help seeking (11 items). Each item formulated a learning-related problem situation that required the application of one specific learning strategy. The instrument offers five different learning strategies to solve each problem, from which participants had to choose one correct strategy for the respective situation. Each correctly identified strategy was scored with one point, resulting in a maximum score of seven points for the time-management subscale, eight points for the effort-regulation subscale, and eleven points for the help-seeking subscale. The number of correctly identified learning strategies was finally converted into the percentage share of correct answers for each subscale.
Resource-management strategy use. To record participants' learning behaviours concerning time management, effort regulation, and help seeking, indicators of their resource-management strategy use were derived from log data collected while students were processing the online learning tool. Time-management strategy use was operationalised as an effective use of study time to achieve previously set learning goals, with learning efforts spread consistently throughout the available study time (Claessens et al., 2007; Orpen, 1994; Pintrich et al., 1991; see also Theobald et al., 2018). Accordingly, effective time management should be reflected by a structured and consistent processing of the online tool's content that enables achievement of the learning goals in accordance with participants' individual learning pace. To quantify this behaviour, deviations from participants' individual learning pace over the course of the online tool's lessons were calculated on the basis of their active processing times per lesson. Since the lessons differed in scope, the recorded processing times were put into the perspective of each lesson's word count, to ensure comparability. Consequently, instead of using non-comparable absolute processing times, participants' reading count of words per minute of learning activity was used as a standardised value for their processing time per lesson. The intraindividual differences in standardised processing times between successive lessons per participant were calculated and averaged to obtain an index of the mean increase in relative processing time per lesson. The following formula was used to calculate the time-management index:
In order to standardise participants' processing times per lesson on the basis of the scope of the respective lessons, the quotient of the corresponding lessons' word count (w) and participants' processing time (t) was calculated for each lesson. These quotients were then used to calculate the differences in participants' standardised processing times between successive lessons. The sum of these differences was finally divided by the number of differences considered (n − 1) to obtain the average change of standardised processing time between successive lessons. Values near zero thus indicated a continuous learning pace in accordance with participants' individual learning conditions. Positive values indicated increasing haste while learning in later lessons (e.g., caused by a perceived lack of time), and negative values indicated a slowdown in learning progress (e.g., caused by distractions or procrastination). Since this index expresses deviations in participants' processing times between successive lessons, it would have to be interpreted as an indicator of inefficient time-management behaviour. In favour of its interpretability, the values are reported reversed for the mediation analysis, in order to obtain the desired index of efficient time-management use.
Effort-regulation strategy use was operationalised as participants' voluntary effort to improve their learning progress after discovering personal deficits regarding the content of certain lessons. For this purpose, each of the online lessons ended with practice questions, which offered the voluntary opportunity to read the relevant section of this lesson again if a question was answered incorrectly, and then to repeat the practice question. The sections for these repetitions were each focused on a short paragraph or figure from the relevant lesson. Alternatively, participants could skip any question that they had difficulties with. The number of questions that were not repeated by participants after giving an incorrect answer was used as an indicator of their effort-related learning behaviour. Since this value expresses how often participants refused to expend effort on correcting their respective learning deficit after being informed of their mistake, it would have to be interpreted as an indicator of low effort regulation. In favour of its interpretability, the corresponding values here are reported reversed for the mediation analysis, so that the indicator represents the extent of participants' effort-regulation strategy use, with larger values indicating greater effort when experiencing learning problems. Overall, this indicator aimed at measuring students' effort even when faced with learning difficult or uninteresting lessons, inspired by items from established inventories such as ‘Even when course materials are dull and uninteresting, I manage to keep working until I finish’ or ‘When course work is difficult, I give up or only study the easy parts (reversed)’ (MSLQ; Pintrich et al., 1991, p. 27).
Help-seeking strategy use was operationalised as participants' ability to obtain information on a particular topic from external sources without receiving any information or assistance in the online learning tool. For this purpose, the last lesson of the online tool provided an unformatted text that had numerous gaps to be filled. Participants were given the task of formatting both the text's references and its bibliography according to a specific guideline that was unknown to them, and on which the online tool offered no further information besides its name. Participants therefore had to independently procure the necessary information to be able to solve the task – for example, by obtaining appropriate literature in the library, by asking lecturers, or by searching the internet. The quality of task performance here was considered as a measure of the success of students' help-seeking skills. Students received one point for each correct response. These points were then converted into values for percentage correctness. This indicator was inspired by items from established inventories such as ‘I look for missing information from various sources (e.g., transcripts, books, professional journals)’ or ‘I look for additional literature when certain content is not yet completely clear to me’ (LIST; Boerner et al., 2005, p. 20).
Academic performance.Academic performance was operationalised as students' performance in an exam on the content of the course at the end of the semester. The exam included 30 questions, each providing one correct answer and three distractors. The questions were evenly distributed with regard to the different lessons of the course and not identical to the practice questions in the online learning tool. Each correct answer was scored with one point, resulting in a maximum score of 30 points.
Control variables. Students' high-school graduation grade point average was assessed as a covariate to control for its assumable confounding impact, as it is one of the strongest predictors of academic performance (e.g., Robbins et al., 2004; Richardson et al., 2012). Due to the orientation of the German grading scale from 1 (very good) to 6 (insufficient), smaller values here indicate better learning outcomes, so the scale was reversed for our mediation analyses for ease of interpretation. Moreover, students' prior knowledge related to the course content was assessed as another covariate, as it may also affect academic performance (Binder et al., 2019, 2021; Simonsmeier et al., 2021). For this purpose, participants were asked to estimate their level of knowledge about the central topics of the lessons in the online course on a scale ranging from 1 (nothing) to 5 (very much). We decided to use a self-assessment to assess participants' course-related prior knowledge and refrained from implementing a pretest in order to not focus them on certain content items before the learning phase (see Waldeyer & Roelle, 2021).
Procedure
Participants' resource-management strategy knowledge and their demographic data were assessed online. To achieve better control over the circumstances of data collection, it was conducted in a large computer lab at the university as part of a course appointment. Item order was varied across students to control for sequence effects. Participants' privacy was protected in accordance with the stipulation of the institutional officer for data protection. The study was embedded in an optional course, and students were not disadvantaged because of non-participation. All data were collected and processed in pseudonymous form.
During the first session, participants were instructed on the procedure of the research study and the course, gave their written consent for participation, and completed the questionnaire on demographic variables. Furthermore, participants' resource-management strategy knowledge was assessed at the beginning of the semester. Subsequently, the online learning tool was unlocked and participants were free to decide when and where to study the provided course content. Log data were collected continuously over the course of the semester to assess participants' learning behaviour. The course ended with an exam on the content learned, which also took place in the university's computer lab. The results of the exam were considered as an indicator of academic performance. Figure 3 provides an overview of the procedure.
Results
Descriptive data
Table 1 provides descriptive statistics and reliabilities for the learning strategy knowledge subscales on time management, effort regulation, and help seeking. The results indicated acceptable internal consistency for all ReMI subscales considered. The proportion of correct solutions given by participants to the presented problem situations demonstrated a rather limited availability of resource-management strategy knowledge at the beginning of the semester, since less than 50% of the items were solved correctly.
The descriptive statistics for the participants' resource-management strategy use indicators are presented in Table 2. The internal consistency was calculated on the basis of students' learning pace in the 12 consecutive lessons (time management), the number of practice questions per lesson repeated after making a mistake (effort regulation), and the points achieved in the research exercises of the last lesson (help seeking). The internal consistency was very satisfactory for all three behaviour indicators. Concerning participants' time-management strategy use, the analysis showed an average increase of 82.7 words per minute (SD = 90.6) over successive lessons, indicating a strong tendency for haste during the processing of subsequent lessons. Thirty-five participants completed the online tool's lessons within the last 12 hours before the respective deadline, and six did not finish it in time. Participants' effort-related strategy use showed that on average they gave incorrect answers to 18.4 (SD = 9.2) of the practice questions and refused to repeat a lesson after giving an incorrect answer in 7.5 (SD = 11.3) of these cases. Evaluation of the help-seeking task showed that participants successfully received and reported an average of 47.4% (SD = 33.0) of the requested information. Participants' performances on this task demonstrated particularly great variation, with 16 participants not being able to gather at least one of the required pieces of information correctly despite sufficient time remaining, indicating remarkable heterogeneity in help-seeking usage.
Finally, exam scores and covariates showed satisfactory internal consistency. The distribution of the exam scores indicated a balanced level of difficulty (M = 18.7, SD = 5.9), ranging from a minimum of 7 points to a maximum of 30 points. Participants' high-school graduation grade point averages included grades ranging from 1.1 to 3.7 (M = 2.4, SD = 0.7), and their course-related prior knowledge ranged from 1.0 to 3.5 (M = 2.1, SD = 0.6; see also Table 3).
Linear regression analyses
Before performing the mediation analyses, we examined the linear relationships between students' resource-management strategy knowledge, resource-management strategy use, and academic performance (see Hayes, 2009). We found that strategy knowledge regarding time management (β = .29, p = .003), effort regulation (β = .30, p = .003), and help seeking (β = .22, p = .022) were significant predictors of the corresponding behaviour indicators for the respective strategy use, and that these behaviour indicators in turn were substantial predictors of academic performance (time management: β = .41, effort regulation: β = .36, help seeking: β = .50, all p < .001). Moreover, participants' resource-management strategy knowledge was a significant predictor of academic performance in the case of time management (β = .28, p = .003) and effort regulation (β = .28, p = .004), but not of help seeking (β = .16, p = .108; for an intercorrelation matrix of the study variables, see Supplemental Materials).
Mediation analyses
We assumed that an indirect effect of students' resource-management strategy knowledge on their academic performance via resource-management strategy use would apply to all three resource-management strategies considered. Accordingly, three mediation models were tested, in which students' academic performance was regressed on their resource-management strategy knowledge, mediated by the respective behaviour indicators for time management, effort regulation, or help seeking strategy use. The mediation models were calculated on the basis of 10,000 bootstrap samples for percentile bootstrap confidence intervals using PROCESS v4.0 (Hayes, 2018). Completely standardised indirect effects were calculated, and the 95% confidence intervals (95%-CI) were computed by determining the indirect effects at the 2.5th and 97.5th percentiles. Given the increased risk of type I error due to multiple testing, all probability values were adjusted using the Bonferroni-Holm method (Holm, 1979).
The results of the mediation analyses indicated that resource-management strategy use mediated the effect of resource-management strategy knowledge on academic performance for all three categories of learning strategies (see Figure 4). Specifically, time-management strategy knowledge significantly predicted time-management strategy use (β a = .29, p = .009), and time-management strategy use significantly predicted academic performance (β b = .36, p < .001). There was a significant indirect effect of time-management strategy knowledge on academic performance via time-management strategy use (β axb = .10, 95%-CI[.03, .19]), but no significant direct effect of time-management strategy knowledge on academic performance (β c' = .18, p = .153). Furthermore, effort-regulation strategy knowledge significantly predicted effort-regulation strategy use (β a = .30, p = .009), and effort-regulation strategy use significantly predicted academic performance (β b = .31, p = .006). A significant indirect effect of effort-regulation strategy knowledge on academic performance via effort-regulation strategy use was observed (β axb = .09, 95%-CI[.02, .18]), but there was no significant direct effect of effort-regulation strategy knowledge on academic performance (β c' = .16, p = .201). Finally, help-seeking strategy knowledge significantly predicted help-seeking strategy use (β a = .22, p = .022), and help-seeking strategy use significantly predicted academic performance (β b = .49, p < .001). A significant indirect effect of help-seeking strategy knowledge on academic performance via help-seeking strategy use was observed (β axb = .11, 95%-CI[.01, .22]), but there was no significant direct effect of help-seeking strategy knowledge on academic performance (β c' = .04, p = .693).
Grade point average and prior knowledge
As expected, the linear regression analysis indicated that participants' high-school graduation grade point average (β = .29, p = .005) and course-related prior knowledge (β = .20, p = .045) were significant predictors of academic performance. Consequently, both variables were added as covariates into the three previously conducted mediation analyses in order to control for their assumed confounding influence. The significant indirect effects of resource-management strategy knowledge on academic performance via resource-management strategy use persisted after taking into account participants' high-school graduation grade point average and course-related prior knowledge in the cases of time management (βaxb = .09, 95%-CI[.02, .17]) and effort regulation (βaxb = .06, 95%-CI[.01, .16]), but not help seeking (βaxb = .05, 95%-CI[−.08, .17]).
Discussion
The aim of the present study was to examine the mediating function of resource-management strategy use in the relationship between resource-management strategy knowledge and academic performance. The assumed mediation model was tested for the three resource-management strategies time management, effort regulation, and help seeking in order to obtain representative data for resource-management learning strategies. We found significant relations with academic performance for both resource-management strategy knowledge and resource-management strategy use for all three resource-management strategies, supporting previous research demonstrating the considerable relevance of resource-management strategies for academic performance (e.g., Richardson et al., 2012; Schneider & Preckl, 2017). The mediation analyses showed a significant indirect effect of resource-management strategy knowledge on academic performance via strategy use, which could be demonstrated for all three resource-management strategies investigated. Finally, these effects persisted after controlling for key predictors of academic performance – i.e., high-school graduation grade point average and prior knowledge – in respect of time management and effort regulation, but not help seeking.
Resource-management strategy use and the self-regulated learning process
Our results showed that students' resource-management strategy use predominantly accounted for the relationship between their resource-management strategy knowledge and academic performance, whereby the direct effect of strategy knowledge on academic performance appeared insignificant after controlling for strategy use. This complements previous findings on the relationships between these learning process components (e.g., Broadbent & Poon, 2015; Donker et al., 2014; Young & Fry, 2008) and provides strong support for the assumed sequence of interrelated and consecutive process phases, as suggested by process models of self-regulated learning (Pintrich, 2000, 2004; Winne & Hadwin, 1998; Zimmerman & Moylan, 2009), at least in the context of resource-management strategies. Moreover, our results underline the potential value of considering more holistic perspectives on students' self-regulated learning skills that go beyond merely knowing about learning strategies and the contextual conditions of their effectiveness. An appropriate definition for this competence comes from Wirth and Leutner (2008), who described self-regulated learning as 'a learner's competence to autonomously plan, execute, and evaluate learning processes, which involves continuous decisions on cognitive, motivational, and behavioural aspects of the cyclic process of learning' (p. 103). This process perspective is supported by recent conceptualisations of competence that describe it as a continuum between knowledge and performance, with performance being the observable behaviour in which competence is manifested (for a review, see Blömeke et al., 2015). Our findings clearly demonstrate the implied need for students to transfer their strategy knowledge into situationally appropriate strategy use in order to be able to improve their academic performance. Given the demonstrated interdependence between the learning process phases in influencing academic performance, it appears particularly problematic that some students cannot transfer available strategy knowledge into adequate strategy use (e.g., Foerst et al., 2017; Veenman et al., 2006). It can be assumed that by interrupting the learning cycle halfway, students are prevented from being able to further develop resource-management skills through learning experience gained, and thereby fail to develop appropriate self-regulated learning routines in the long term. This highlights the importance of practice in the use of resource-management strategies for students, in order to make use of available strategy knowledge in a way that can positively affect their academic performance.
Measurement of students' resource-management learning strategy skills
Our operationalisation of students' resource-management strategy skills aimed to provide authentic insights into students' learning processes in a real academic learning context. Consequently, we used measures based on knowledge test data and indicators of observable learning behaviour in order to avoid potential issues associated with self-reported data, such as response bias, lack of context, or limited accuracy of students when reporting their own learning strategy behaviour (Cleary et al., 2015; Paulhus & Vazire, 2007; Winne & Jamieson-Noel, 2002). By designing behaviour indicators based on log data for our resource-management strategies of interest we sought to implement an online trace method for assessing students' resource-management strategy use, to achieve a closer approximation of their actual learning behaviour and to reduce their awareness of (and influence on) the ongoing data collection (Azevedo & Gasevic, 2019; Rovers et al., 2019; van Halem et al., 2020). Taking into account the requirements of given learning situations when measuring students' resource-management strategy use should help to assess not only the quantity but also the quality of their learning behaviour, as reflected by the fit between strategy use and situational demands (Wirth & Leutner, 2008). The significant relationships of all three behaviour indicators with both resource-management strategy knowledge and academic performance can be seen as strong support for their validity as measures of students' resource-management strategy use in authentic learning situations.
By considering participants' active processing times in relation to the word count of the corresponding lessons, the time-management strategy use indicator represents a measure of intraindividual fluctuations in students' learning pace throughout the data collection period that is sensitive to individual learning preconditions (e.g., varying reading skills). These observable fluctuations in students' processing times represent deviations from their optimal learning pace and thereby take into account poor time-management behaviours that, despite not being directly observable, might affect the time data collected: for example, distractive behaviours (e.g., using smartphones, clicking on entertaining websites) or skimming through lessons (e.g., due to running out of time when approaching the deadline; see Steel, 2007). This focus on participants' individual learning pace also means that the indicator allowed an assessment of students' planning and learning behaviour under consideration of their individual learning preferences, instead of focusing solely on time data (e.g., time spread of learning activities during data collection, or distance of last learning activity to the deadline). As a result, it allows an assessment of very different time preferences in students' planning behaviour beyond the mere consideration of learning dates, since any learning activity can be in time as long as these learning activities are planned thoroughly enough to still have enough remaining time left to achieve set learning goals in a structured manner.
The effort-regulation strategy use indicator was our attempt to transfer effort-regulation items from established self-report inventories (e.g., MSLQ; Pintrich, et al., 1991) into an observation of actual learning behaviour by quantifying participants' voluntary effort to advance their learning progress after experiencing learning problems in the online tool. Thereby it allowed the observation of students' learning behaviour in an authentic study situation, since the practice questions provided were directly relevant to the exam at the end of the course, which determined students' course grades. Our help-seeking strategy use indicator aimed to provide insights on students' ability to obtain information from external sources on their own when the necessary information is not provided by lecturers or in course materials. Given their low average prior knowledge on the topic, every piece of information that participants were able to report correctly here had to be acquired from an external source (e.g., library, fellow students, internet), and thus can be interpreted as an indicator of their help-seeking skills.
Limitations and research suggestions
The self-regulated learning process includes three essential phases in which students need knowledge about learning strategies and the contextual conditions for their effectiveness, in order to be able to actually use this knowledge in learning situations, and to evaluate their strategy use afterwards to further develop their strategy knowledge and optimise future learning processes (Zimmerman & Moylan, 2009). Apart from our comprehensive operationalisations of resource-management strategy knowledge and its use, the self-reflection phase was not explicitly considered in our data collection. Due to our long-term observation of numerous learning cycles over the 13 course lessons, however, the results of students' self-reflections were recorded indirectly through their impact on subsequent learning behaviours as part of the log data. Therefore, it can be assumed that this was not detrimental to the collection of relevant data, since students needed to process (and reflect on) a variety of tasks over the several months of data collection while learning to complete the online course successfully. However, for future research its explicit consideration could offer valuable additional details for a deeper understanding of self-regulated learning processes. The same applies to replicating our findings regarding the mediating role of students' strategy use in the relationship between their strategy knowledge and academic performance. Here, similarly, replications based on other categories of learning strategy (i.e., cognitive and metacognitive) could add useful further insights to the current state of research on the self-regulated learning process. In this context, students' motivation while learning with an online course might be a particularly promising addition. Students' motivation is considered an important aspect of self-regulated learning (Pintrich, 2004; Zimmerman & Moylan, 2009), which also contributes to academic performance (Kryshko et al., 2020; Schwinger & Stiensmeier-Pelster, 2012). Accordingly, students need not only knowledge of learning strategies and the skills to use them, but also the willingness to do so. It is therefore assumed that students' current motivation moderates the relationship between their strategy knowledge and strategy use in specific learning situations (Thillmann et al., 2013). Thus, including students' current motivation as a moderator variable in our mediation model could be a meaningful addition for future studies.
An interesting finding regarding the help-seeking indicator was the significant indirect effect observed when considering help-seeking strategy use concerning the connection of help-seeking strategy knowledge and academic performance, given that the total effect found before was not significant. Furthermore, this indicator showed a noticeably stronger connection to academic performance than to help-seeking strategy knowledge. Given the assumption that resource-management strategy knowledge influences performance through resource-management strategy use, these relationships may suggest the influence of a confounding variable. This could be due to a too superficial operationalisation of students' help-seeking behaviour. Other than the indicators for time-management and effort-regulation strategy use, which directly referred to participants' learning behaviour during specific learning situations, the help-seeking strategy use indicator was based on the outcome of participants' use of help-seeking strategies. Hence, participants needed to independently acquire the required information from external sources, but additionally had to apply it correctly to the task. The indicator of students' help-seeking behaviour was thus partially based on a performance task that is reminiscent of transfer tasks students would know from school, which may have distorted the observed effects. This ability of students to acquire and transfer the required knowledge should, however, be reflected by their high-school graduation grade point average. Indeed, adding students' high-school graduation grade point average as a covariate provided strong support for this assumption and demonstrated an inferiority of the help-seeking indicator in predicting academic performance. Accordingly, it cannot be ruled out that this indicator for help-seeking strategy use lacks appropriate differentiation between students' actual help-seeking behaviour and the performance required to demonstrate it. For future investigations, a behaviour indicator for students' help-seeking strategy use should be implemented that enables online measurement of students' help-seeking behaviour without relying in part on performance to assess students' help-seeking skills.
Conclusion and practical implications
The present study showed that students' ability to use resource-management strategies is an indispensable link in explaining the relationship between their strategy knowledge and academic performance. By giving further insights into the interrelations between these central components of the learning process, the study provides important evidence on one of the core assumptions of established theories on the process of self-regulated learning (e.g., Schmitz & Wiese, 2006; Zimmerman & Moylan, 2009), and thereby offers important implications for research and training practice. For pertinent investigations, it implies an advantage of instruments that provide an assessment of both knowledge and behavioural aspects in their operationalisations of the self-regulated learning strategies they aim to assess (e.g., Waldeyer, et al., 2020; see also, Rovers et al., 2019). For training practice, it implies that interventions addressing both students' strategy knowledge and strategy use should be a more effective way to improve academic performance than interventions aimed at individual phases of the learning process. This is supported by previous evidence, which indicates that even when students' strategy knowledge is sufficiently available, their actual use of the corresponding strategies in relevant learning situations can be deficient (e.g., Cao & Nietfeld, 2007; Foerst et al., 2017; Waldeyer, et al., 2020).
Consequently, conveying strategy knowledge alone might not be a reasonable design for effective interventions. Instead, these findings indicate that the focus should be more on the transfer of strategy knowledge into strategy use, and practice in doing so. For resource-management knowledge instruction this implies the particular relevance of conditional (when to use a strategy) and procedural (how to use a strategy; Weinstein et al., 2000) strategy knowledge in providing students with the theoretical foundations for successful strategy use. For resource-management practice this implies a need for instructional support to guide students during the process of developing and consolidating effective learning routines (Lee et al., 2010; Müller & Seufert, 2018; Roelle et al., 2017; Thillmann et al., 2009; Wirth, 2009), and motivation to do so (Kryshko et al., 2020; Thillmann et al., 2013). Overall, our results provide support for the relevance of resource-management strategies for freshmen students, and indicate a great potential for instruments as well as for interventions aiming both at students' strategy knowledge and their use of the corresponding strategies in promoting academic performance.
Electronic supplementary material
The electronic supplementary material (ESM) is available with the online version of the article at https://doi.org/10.1024/1010-0652/a000374
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