The Interplay of Self-Regulated Learning, Cognitive Load, and Performance in Learner-Controlled Environments

Abstract

Learner control allows for greater autonomy and is supposed to benefit learning motivation, but it might be more advantageous for students with specific learner characteristics. The current study looks into the relationships between self-regulated learning, cognitive load, and performance within learner-controlled environments. The research was conducted in an asynchronous online setting, allowing for learner control. Cognitive load and self-regulated learning were measured using self-report questionnaires. Performance was assessed through case solutions. The participants were 97 graduate law students studying the civil code. Analysis based on structural equation modeling showed that both prior knowledge and self-regulated learning skills significantly contribute to the increase in germane cognitive load and are positively correlated with performance. The implications of these findings underscore the critical role of prior knowledge and self-regulated learning skills in shaping the cognitive processes involved in learning, ultimately impacting academic achievement. These results emphasize the need for careful consideration of learner-control options in asynchronous online environments.

1. Introduction

Recent advancements in computer-supported and digital learning, especially in asynchronous formats, have led to the increasing popularity of learner-controlled settings in which students are provided with opportunities to select the activities, the order, and the pace in which they prefer to interact with the content [1]. It is assumed that learner control will lead to several beneficial educational results, such as an increase in motivation [2], customized instruction that caters to individual needs and interests, the creation of effective teaching methods, and a sense of personal control [3]. However, learner control does not always result in enhanced learning outcomes [4]. Moreover, previous research shows that it may only be advantageous for learners with certain individual characteristics or when used under specific circumstances, for example, learners with high prior knowledge [3] or conditions in which it does not generate excessive cognitive load [5].

The autonomous nature of asynchronous online learning environments requires a certain level of self-regulated learning skills, such as setting goals, planning, and monitoring [6,7]. Self-regulated learning is particularly significant in learner-controlled environments, as these environments require that students realize their limitations and have the ability to organize their learning in such a way as to meet them [8]. It is, therefore, possible that this self-regulatory challenge causes problems for students [9]. If self-regulated learning consumes all of the students’ mental resources, learning might be hampered, as no resources would be available for processing new information in their working memory and transferring it to long-term memory.

One way of understanding how students use their mental resources in a learner-controlled environment is with cognitive load theory. This theory has been used as a framework to understand what is happening in learner-controlled environments [2,4,9,10,11]. Cognitive load represents information to be processed given the limited amount of available cognitive resources [12]. The objective of cognitive load theory is to explain how the cognitive load imposed by learning can impact students’ capacity to process new information and build knowledge by transferring information from working memory to long-term memory [13]. The theory posits that learning is optimized when the instructional design takes into account the limitations of working memory and reduces the need to process information not relevant to learning, thereby promoting schema acquisition [14]. Originally, researchers classified cognitive load into three types: intrinsic load, which can be broadly defined as the complexity of the content due to the number of interacting elements combined with learners’ prior knowledge; extraneous load, which can be broadly defined as poorly designed instruction that induces learners to process information not relevant to learning the task; and germane load, which can be broadly defined as the availability of cognitive resources directed at dealing with intrinsic load, with instructional interventions aimed at minimizing extraneous load and enhancing germane [12]. Cognitive load theory aims to provide recommendations to researchers and practitioners on how to design efficient instruction and facilitate learning [15].

There is evidence that the use of learner-control options can require additional cognitive resources [2], and specific cognitive load elements (intrinsic load, extraneous load, and germane load) are affected by the learning task and the type of learner-control options used to complete the learning task [2,11]. The goal from a cognitive load perspective is to help learners deal with intrinsic load and mitigate excessive extraneous load so that germane load can be promoted [13], which previous research suggests is possible in learner-controlled environments if the learner-control options allow learners to more efficiently process information required to complete a task [11]. The investigation of effects on cognitive load in learner-controlled settings ultimately contributes to the better instructional design of these environments [4].

2. Literature Review

2.1. Self-Regulated Learning in Learner-Controlled Environments

Self-regulated learning refers to the ability of students to plan their learning activities and monitor, control, and assess their progress in order to apply the most efficient learning strategies and modify them as needed [7]. Pintrich and de Groot (1990) [16] consider that self-regulated learning has at least three major components: metacognitive strategies, students’ control of the effort to deal with learning tasks, and actual cognitive strategies to learn the material. Self-regulation, in this specific context, refers to the deliberate and strategic efforts undertaken by students to manage their learning; however, it is only one of the components of self-regulated learning.

Research indicates that students who exhibit strong self-regulatory skills tend to resort to more sophisticated cognitive strategies, allowing them to demonstrate better academic performance [16]. As a result, understanding and promoting self-regulated learning has become increasingly important in educational settings to equip students with the necessary skills to effectively navigate their learning experiences. Self-regulated learning is assumed to be crucial for online environments due to the level of autonomy associated with online learning [6], particularly for learner-controlled environments [8], as it enables learners to navigate the learning materials effectively and manage their cognitive load. Moreover, it is suggested that learner control can have a significant impact on the quality of online learning [17].

It is also important to note that the self-regulated learning strategies themselves can, at times, help students avoid extraneous processing, as such strategies are used to select content in a way that prevents them from processing extraneous information [18]. Another characteristic is prior knowledge, which plays a significant role, as novice learners cannot process new information and benefit from learning strategies at the same time [10]. Nevertheless, distributing limited mental resources between learning and using self-regulated learning skills might create a challenge [19]. Previous research suggests that self-regulated learning predominantly hinges on working memory processes when learners lack sufficient prior knowledge and have low levels of self-regulated learning skills [20], creating cognitive overload.

2.2. Cognitive Load in Learner-Controlled Environments

One of the factors that has a positive impact on cognitive load while self-regulating in learner-controlled environments is prior knowledge, as learners’ familiarity with the material makes these complex environments more comfortable for them. Novice learners cannot process new information and benefit from learning strategies at the same time [10]. Previous research has shown that learners with high domain knowledge will benefit more in a learner-controlled environment than learners with low prior knowledge [3], which is most likely due to the fact that learners with low prior knowledge have to learn new information and make choices about how to proceed with their instruction at the same time [4]. Moreover, it is suggested that compared to learner-controlled environments, instructor-controlled environments may be more beneficial for novice learners due to the instructional guidance received when implementing solutions [10].

The nature of intrinsic cognitive load lies in the difficulty of particular content in terms of the number of interacting elements for learners, taking into account their domain prior knowledge [13]. In general, it is assumed that intrinsic load is lower for learners with higher levels of prior knowledge as, for an expert learner, this material would be easier to interact with than for a novice learner [21]. In the case of learner-controlled environments, this effect could be exaggerated: intrinsic load might be lower for learners with more prior knowledge due to the way in which they can effectively manipulate the content to be learned [2]. Learner-controlled environments allow learners to control the amount of content processed at a single time through the selection of content and the way in which they process it. For example, learners can select the content to be learned and then pause or segment that content, effectively breaking down element interactivity so that they can process isolated elements of the content before moving on [18,22]. While the breakdown of element interactivity does not directly reduce intrinsic load when confronted with the entire task [23], the intrinsic load is lower during the learning of the task due to the breakdown of element interactivity [24]. From this perspective, selecting the way in which to process the content may allow learners to control the number of interacting elements processed at a single time, thus processing content with lower levels of intrinsic load while learning a specific task. This is particularly advantageous for learners with higher prior knowledge in that using learner control to isolate elements of content so that it matches one’s domain knowledge takes a higher level of expertise in the content itself [5]. Furthermore, such an approach to information processing requires the use of self-regulated learning skills as highly self-regulated learners are able to self-manage their load, optimally processing suboptimal instruction as well as managing the number of elements interacting at a specific time [25]. Due to the nature of intrinsic load, a negative relationship between high levels of intrinsic cognitive load and performance might be expected; however, high levels of prior knowledge might mitigate this effect.

According to cognitive load theory, extraneous load occurs because of poorly designed instruction, causing learners to spend more of their mental resources on processing information that is irrelevant to learning [26]. It is expected that expert learners having constructed schemata stored in their long-term memory are less constrained in working memory resources and are thus better able to overcome extraneous load or self-manage it [27]. With that being said, an expertise reversal effect can occur if expert learners waste an exorbitant amount of cognitive resources on information they already know, creating higher levels of extraneous load [28]. However, those with expertise in the subject matter should be more likely to avoid such a situation in learner-controlled environments, as it is the expertise of learners that allows them to alter the element interactivity by controlling how they process content to match their domain knowledge [5], thus avoiding the processing of redundant information and ensuring that their cognitive resources are directed toward processing information relevant to the task [28]. Additionally, one noteworthy point is that according to the element interactivity effect [29], when instructional materials consist of multiple elements that interact with one another, extraneous load can increase due to poor instructional presentation, creating the inability to manage high levels of intrinsic load, which has implications for both working memory and long-term memory regardless of learners’ prior knowledge [21]. There is an assumption that extraneous cognitive load leads to poor performance, as learners’ working memory resources are distracted and allocated to overcoming badly designed instruction. Learners have to process unnecessary information, increasing the element interactivity, which impedes learning and results in unsatisfactory learning outcomes [15].

The conceptualization of germane load has changed over time. At one point, it was understood as mental effort directed to the processes that contribute to learning [30]. However, it has since been reconceptualized as the availability of mental resources allocated to manage intrinsic load [12]. This is due to studies that have shown that mental effort does not add to the internal consistency of the load [12]. Furthermore, while it has been described as no longer being an independent source of load due to its association with intrinsic load, it has further been postulated that it may be more accurately described as a representation of understanding [12].

Previous studies [11] suggest that in a learner-controlled environment, students’ understanding is enhanced as they engage their mental resources while deliberating on their choices, ultimately contributing to the learning experience. Learner control also contributes to the deeper processing of information, given the availability of cognitive resources, as it requires students to mentally reorganize information [2]. Additionally, learners’ characteristics ultimately determine their ability to understand the content, as highly motivated learners are more likely to invest available resources into learning [31]. For example, prior knowledge is viewed as an important individual characteristic of learners; it changes the way learners process information, and it is assumed that higher levels of knowledge provide them with abilities to invest more resources in learning [21]. Thus, expert learners are more likely to change the way in which they process information by investing more cognitive resources directed to selecting appropriate content to match their domain knowledge within learner-controlled environments [5]. Moreover, prior knowledge can assist learners in making strategic choices and thus facilitate their learning in learner-controlled environments [1]. Learner-controlled environments promote increased mental processing of the content [32], which is germane to learning and thus results in better performance. Previous research has shown that in a learner-controlled environment, students perform better in high element interactivity material [11], as germane load is fostered, ensuring better results.

2.3. Self-Regulated Learning and Cognitive Load

Learners’ level of self-regulated learning allows them to overcome challenges, as by employing self-regulated learning skills, learners are empowered to identify and address the intrinsic complexity of the material [33]. Furthermore, there is a relationship between self-regulated learning and the use of learner-control options available in asynchronous online environments [34]. It may be that self-regulated learners experience less intrinsic load because they are more likely to use learner-control tools, allowing them to break up element interactivity [24]. It has been previously suggested that learners self-manage their cognitive load, guiding their cognitive resources to the most effective use, enabling them to extract maximum learning outcomes [25]. Additionally, it has been found that self-regulation used in learner-controlled environments allowed learners to overcome high levels of intrinsic load and produce higher levels of germane load, thus indicating that even if the learners do not necessarily reduce the element interactivity, other self-regulated learning strategies may help them overcome the high levels of intrinsic load without reducing the load [35]. Furthermore, it has been suggested that when learners make judgments of their effort based on intrinsic cognitive load, they can be more motivated to use self-regulated learning strategies [36].

Extraneous load should be a primary concern of instructional design [21]; it is the aspect of cognitive load that researchers and practitioners need to focus on in order to improve the learning experience and learning outcomes. On the other hand, it is possible that even when the instruction is poorly designed, learners might achieve acceptable results. It provokes the question: could there be certain individual characteristics that allow learners to perform even under unfavorable circumstances? According to previous research, the level of self-regulated learning skills equips learners with a variety of metacognitive strategies, such as planning, monitoring, and controlling their learning process, which help learners overcome challenging learning environments [2]. However, if extraneous load is a base for making monitoring judgments, it increases invested effort without adding to learning performance [36]. Although self-regulated learning skills are used to overcome challenging environments, a study by Lange et al. [37] showed that learner-controlled asynchronous environments containing high levels of extraneous load produced a weaker relationship between self-regulated effort and germane load, while the relationship was much stronger in low extraneous load learner-controlled environments. The same study showed a negative relationship between germane load and extraneous load and a positive relationship between self-regulated effort and germane load, as expected. While these relationships were expected, it was somewhat surprising to see that self-regulated effort had a much weaker effect during poorly designed instructional situations within a learner-controlled environment.

Learner control presumes that students take specific actions requiring self-regulated learning skills, which, in turn, are expected to lead to the availability of more germane resources that are beneficial for learning [1]. Germane cognitive load represents the availability of resources that can be directed toward intrinsic load, and self-regulated learners tend to take advantage of the resources that foster greater understanding [38]. It is no surprise, therefore, that highly self-regulated learners are better able to direct their resources more efficiently and be more productive. Previous research has shown that in learner-controlled environments, students have to apply self-regulated learning skills, i.e., monitoring, self-assessing their progress, and making their choices based on that assessment [39]. This is due to the fact that the effort associated with self-regulated learning is often context-dependent [40], and learners generally apply effort at higher levels in environments in which they believe they can succeed [41]. This is evident in learner-control asynchronous online environments containing more autonomy, as self-regulated learners are more likely to apply more effort in such environments because they believe that taking advantage of the autonomous nature will lead to success [42]. Empirical evidence supports the notion that self-regulated learning leads to a better understanding of the content through better planning and effort in the use of learner-control options in multimedia online environments [43,44].

3. The Current Study

Previous research lacks clarity in describing the interaction between prior knowledge, self-regulated learning skills, and cognitive load in learner-controlled environments. While there is empirical evidence that learner control might induce additional cognitive load and that students with higher self-regulated learning skills perform better in learner-controlled settings, the nature of relationships between these constructs remains unexplored. The current research is thus aimed at investigating the fundamental characteristics of connections that emerge between prior knowledge, different types of cognitive load, and self-regulated learning skills and how they might impact performance in a learner-controlled environment. The present study attempts to answer the following questions and test the associated hypotheses:

Q1. 

What is the relationship between prior knowledge and the differing aspects of cognitive load in a learner-controlled environment?

H1a. 

Prior knowledge has a negative relationship with intrinsic load.

H1b. 

Prior knowledge has a negative relationship with extraneous load.

H1c. 

Prior knowledge has a positive relationship with germane load.

Q2. 

What is the relationship between self-regulation and the differing aspects of cognitive load in a learner-controlled environment?

H2a. 

Self-regulation has a negative relationship with intrinsic load.

H2b. 

Self-regulation has a negative relationship with extraneous load.

H2c. 

Self-regulation has a positive relationship with germane load.

Q3. 

What is the relationship between the differing aspects of cognitive load and performance in a learner-controlled environment?

H3a. 

Intrinsic load has a negative relationship with performance.

H3b. 

Extraneous load has a negative relationship with performance.

H3c. 

Germane load has a positive relationship with performance.

4. Materials and Methods

The primary focus of this research is to examine the relationship between students’ prior knowledge, different types of cognitive load, and their academic performance in a learner-controlled online environment. The study was conducted with an introductory class of a course on Data Protection Law and designed for university students who take the course during the first and second years of their master’s degree in law.

4.1. Participants

The participants gave their consent to participate in the research. They were informed that the performance assessment, which accounted for 20% of their final course grade, was a component of the study. The participants were studying at a large urban university in Russia. Although 108 students were enrolled in the course, only 97 attended the class, and 1 student decided not to answer the questionnaires. With an average age of 23, the group consisted of 56 females and 41 males. All students enrolled in the course had acquired a bachelor’s degree in law, which provided them with a deep understanding of the core legal concepts of the Civil Code, which is fundamental for learning data protection law. Such a sampling approach helped to ensure approximately an equal level of knowledge in the field of legal studies among the respondents.

4.2. Materials

Cognitive load was assessed using a student self-report survey (10 items using an 11-point Likert scale). The survey used a tool developed by Leppink et al., [30] with all items translated into Russian with respect to commonly used vocabulary for such learning situations. The items measuring cognitive load are listed in Appendix B. This survey aims to measure the three types of cognitive load: intrinsic, extraneous, and germane cognitive load. The internal consistency of the three types of cognitive load was estimated using Cronbach’s alpha. Cronbach’s alpha for the three types of cognitive load are intrinsic load (0.865), extraneous load (0.782), and germane load (0.913) (

Table 1. Mean inter-item correlation for self-regulation and cognitive load scales.

Statistic	N Items	Mean Inter-Item Correlation	Cronbach’s Alpha
Self-regulation	6	0.17	0.54
ICL	3	0.68	0.87
GCL	4	0.73	0.91
ECL	3	0.55	0.78

).

Self-regulated learning skills consist of several components, including self-regulation comprising metacognitive strategies and effort management. The latter was measured using an adaptation of the Motivated Strategies for Learning Questionnaire (MSLQ) developed by Pintrich and de Groot (1990) [16] and widely validated in the previous literature, consisting of 5 scales (44 items using a 7-point Likert scale). This survey is intended to measure the types of learning strategies and academic motivation used by college students; however, for the purpose of the current study, only one scale, “Self-regulation”, was used, consisting of 6 items. Self-regulation, in this context, can be interpreted as the cognitive processes that students engage in to monitor and control their learning, thus generating a regulatory effort that is linked to mental effort and cognitive load. This scale, as well as its title, is referred to throughout the study; however, it is only one of the components of self-regulated learning. The items included in the scale are listed in Appendix A.

The learning and assessment materials were developed for this research based on the previous study of Costley et al. (2024) [45]. The study compared a problem-solving first sequence to a direct-instruction first instructional sequence in legal sciences. The materials developed for the current study have been further fine-tuned and adapted for asynchronous learning environments. The materials included a test on prior knowledge that included 10 multiple-choice questions covering various aspects of civil law that are relevant to the topics being studied. Three subtopics comprising four types of materials each were covered: a video lecture providing explicit instruction on the topic; a video presenting a worked example illustrating a step-by-step procedure on how the broadly defined legislative interpretation of personal data can be implemented for a particular situation and category of data; a problem-solving task in the form of a similar legal case; and finally the solution to this legal case that follows the same step-by-step procedure. This multidimensionality resulted in a relatively low Cronbach’s alpha (0.42), with the mean inter-item correlation equaling 0.082, which is a common problem of wide-ranging conceptual knowledge tests [46].

Academic performance was assessed through a set of legal cases. Students were required to give a well-reasoned legal analysis for each case, determining whether certain data should be classified as personal or not, especially when the law is unclear or lacks specific guidance on the matter. While solving each case, students had to give the correct solution, implement the correct legal terminology, and use reasoning based on the existing legislature. Learning outcomes were evaluated in three dimensions: solution (whether the data was qualified correctly—2 points), argumentation (how substantive were the arguments that supported the solution—3 points), and terminology (whether the legal terminology was appropriately applied—1 point). These dimensions represent important skills for legal students [45]. The maximum score was 36 for all 6 cases for all three dimensions. Both the instructor and his teaching assistant graded all documents for reliability. If there was any discrepancy in scores, the work was discussed until a score was agreed upon. During the assessment, learners had no access to other materials.

4.3. Procedures

While the course took place offline and comprised standard lectures and seminars in smaller groups, the class for the research was organized in an online educational setting that would allow for learner control. The class aimed to explore the concept of personal data from a legal standpoint. While it was important to equip students with a fundamental comprehension of the types of information classified as personal data in accordance with legal regulations, considering regulatory perspectives and real-world legal applications, it was also essential to teach students to distinguish whether specific information qualifies as personal data when not directly stated in the law.

Pre-recorded learning materials and surveys were uploaded to the website specifically designed for the current research. The full illustration of all the materials is presented in Figure 1. Participants were instructed that the website interface and organization of the learning materials offer flexibility to select the preferred methods of engaging with the content. Students could take actions that they considered important to learn the new information and pass the final assessment within the time limit of 2,5 h. Thus, the learner control in this study included watching, rewinding, fast-forwarding, and pausing the pace of the videos with explicit instructions and worked examples; attempting to solve the cases representing problem-solving tasks; downloading the canonical solution to the case; and choosing the order of all materials.

5. Results

The means and standard deviations of the main variables of self-regulation, prior knowledge, intrinsic cognitive load, extraneous cognitive load, germane cognitive load, and performance can be found in

Table 2. Descriptive statistics.

Statistic	N	Mean	St. Dev.	Min.	Max.
Self-regulation	96	4.79	0.95	2.50	6.67
Prior knowledge	96	7.61	1.64	2	10
ICL	96	4.06	2.12	0.00	10.00
GCL	96	8.37	1.68	0.00	10.00
ECL	96	2.13	1.91	0.00	10.00
Performance	97	20.97	4.89	7	34

.

To validate the proposed hypotheses, structural equation modeling (SEM) with a maximum likelihood estimator was employed [47]. This approach was chosen to comprehensively evaluate the intricate relationship between students’ cognitive load and their performance while also controlling for their prior knowledge and self-regulation skills. Since our hypotheses required a more comprehensive approach to capture the complex interplay between cognitive load, prior knowledge, self-regulation skills, and performance, SEM allowed us to model these intricate relationships holistically, providing a more detailed and nuanced understanding of the factors influencing students’ learning outcomes. We did not use mediation analysis because our primary goal was to understand the direct and indirect relationships among multiple indicators simultaneously rather than focusing on a single mediating pathway. To mitigate the potential limitations associated with a relatively small sample size and the variables’ distribution specifics, we employed the Bollen-Stine bootstrap method [48] to obtain robust estimates and reduce the risks of underpowered analysis. The use of p-values derived from the Bollen-Stine bootstrap further enhanced the validity and robustness of our analysis.

The findings from the structural equation modeling indicate that some aspects of students’ cognitive load are determined by their previous knowledge and self-regulated learning skills (Figure 2). Specifically, students who entered the course with a higher level of previous knowledge demonstrated a significantly higher level of germane cognitive load (β = 0.24, p-value < 0.05). Additionally, students with more developed self-regulated learning skills also exhibited a significantly higher level of germane cognitive load (β = 0.25, p-value < 0.05). However, no significant relationship was found between other types of cognitive load and these variables. These findings provide support for Hypothesis 1c (Prior knowledge has a positive relationship with germane load.) and Hypothesis 2c (Self-regulated learning has a positive relationship with germane load.).

Furthermore, we explored the internal structure of cognitive load, examining relationships between its different aspects. The results reveal a significant positive relationship between intrinsic load and extraneous load (β = 0.39, p-value < 0.01). Additionally, a significant negative relationship was observed between extraneous load and germane load (β = −0.18, p-value < 0.10). Lastly, the study investigated the effect of different types of cognitive load on students’ final performance. The results indicate that only germane cognitive load showed a significant positive linear relationship with student performance (β = 0.22, p-value < 0.05), supporting Hypothesis 3c (GCL has a positive relationship with performance.). However, no statistically significant relationships were found for other types of cognitive load. Finally, self-regulation showed a significant positive relationship with prior knowledge (β = 0.20, p-value < 0.10).

6. Discussion

This research investigated the complex relationships between prior knowledge, different types of cognitive load, and self-regulation, including metacognitive strategies and effort regulation as a component of self-regulated learning skills, and how they might correlate with performance in a learner-controlled environment. The findings of this study suggest that learners’ individual characteristics, such as prior knowledge and self-regulated learning skills, are important factors affecting germane cognitive load in learner-controlled environments. On the other hand, our hypotheses on the direction and strength of the relationships between intrinsic and extraneous types of cognitive load and prior knowledge, self-regulation, and performance were not confirmed by the results of the current study.

Intrinsic load was expected to have negative relationships with prior knowledge, self-regulation, and performance (Hypotheses 1a, 2a, and 3a). It was hypothesized that prior knowledge would have a negative relationship with intrinsic cognitive load, but this hypothesis was not confirmed. This does not fall in line with the fact that learning materials are likely to create fewer challenges for an expert learner compared to a novice learner [49], and learners with higher expertise are better able to manipulate the task, thus reducing content complexity during instruction [24]. Self-regulation was predicted to have a negative impact on intrinsic load due to the likelihood that self-regulated learners use learner control in ways that limit element interactivity [24], but the results of the current study are not in line with this assumption. Regarding the results involving the relationship that intrinsic load has with both prior knowledge and self-regulation, it could be that the learner-control options used in this study were effective in allowing learners to process a limited amount of elements at a single time regardless of prior knowledge and self-regulation levels. As for performance, it was assumed to have a negative relationship with intrinsic load, but the findings of the study do not support this hypothesis either. This may be accounted for by the level of complexity of the learning materials used in this study, as well as the general knowledge across the group. The average level of intrinsic load (4.06) reported by the participants could be regarded as optimal, i.e., neither too easy nor too difficult for learners. The results of the test on prior knowledge (7.61) might indicate that the group of students in this study have enough prior knowledge to successfully manage their intrinsic load.

It was anticipated that extraneous load would have a negative relationship with prior knowledge, self-regulation, and performance (Hypotheses 1b, 2b, and 3b). Previous research has shown that learners with substantial prior knowledge can effectively organize and integrate new information as well as effectively manage element interactivity through appropriate content selection in learner-controlled environments, resulting in reduced extraneous cognitive load [13,24]. Managing extraneous load becomes crucial when the content has high element interactivity and presents high intrinsic load for learners, but once the intrinsic complexity of the material does not overwhelm learners and impede information processing, managing extraneous load becomes achievable for students with different levels of expertise [28]. The intrinsic complexity of the materials for the current study is optimal, as the levels showed that the content was not overly simple or complex, and it may be the reason why prior knowledge has no relationship with extraneous cognitive load. The design of the materials was effective and did not require learners to use high levels of self-regulated learning skills, which is supported by the level of extraneous cognitive load (2.13). This fact might also explain why the results do not provide evidence of the negative relationship between extraneous load and performance. The instruction did not excessively strain working memory capacity and did not impede learning. In a learner-controlled environment, it could imply that even if learner control did impose additional load, overall working memory capacity was not exceeded [50], and thus, there are no relationships between extraneous cognitive load and performance.

It is of interest that the extraneous load in this study, while not intentionally manipulated, was low, and it had a relationship with the relatively low levels of intrinsic load. It is generally considered that because intrinsic load represents the difficulty of instruction and extraneous load represents the clarity of instructional delivery [12,13], the general consensus is that they are independent of each other, as they reflect different sources of load. In the case of the present study, extraneous load was significantly low with intrinsic load leaning toward the low end as well. Future research may want to consider direct manipulation of the loads. This could potentially solve two issues: 1) provide clarity as to what sources of loads are actually being examined, and 2) allow for the control of the amount of extraneous load in the environment to see how specific levels of extraneous load affect the relationships in this study.

Germane cognitive load refers to the actual working memory resources dedicated to managing intrinsic cognitive load [13]. We predicted that germane load would have positive relationships with all variables: prior knowledge, self-regulation, and performance (Hypotheses 3a, 3b, and 3c). Since prior knowledge considers mental schemas available in long-term memory, it is expected that having more schemas allows learners to allocate their mental resources to processing new information, rendering the mental effort germane. The results of this study provide insights into learner-controlled environments and generally support these expectations, showing a positive relationship between prior knowledge and germane cognitive load. Self-regulation shows a positive relationship with germane load as well; it can be attributed to the fact that self-regulated learners possess a repertoire of cognitive and metacognitive strategies that enable them to manage their learning processes effectively. Moreover, previous research [20] suggests that high levels of self-regulated learning skills imply that the skill is performed automatically, thus reducing cognitive load. Furthermore, self-regulated learners are more likely to engage in deep processing and employ effective learning strategies, including the strategic use of learner-control options when presented with such tools [51], which further contributes to the enhancement of germane cognitive load and better learning outcomes due to learners’ ability to process information directly relevant to the learning task [52]. This is also confirmed by the findings of the current study, which show a positive relationship between germane load and performance. The allocation of cognitive resources toward germane load facilitates the integration of new information with prior knowledge, promoting the creation of schemas and ultimately leading to better performance.

Our results show no relationship between germane load and intrinsic load, going against research that states germane load is actually the availability of resources directed at intrinsic load. However, looking at the mean difference between the two loads in this study, 8.37 for germane and 4.06 for intrinsic, it can be inferred that although no relationship was established, the relatively low amount of intrinsic load presented the learners with more cognitive capacity for germane processes.

The relatively low levels of cognitive effort created through low intrinsic and extraneous processing most likely left more room to process relevant information that would lead to understanding. It may be that because the instruction was generally not unclear/confusing or difficult, this would leave the learners with more room to direct their processes to understanding the material. Along the lines of what Leppink et al. (2014) [12] have postulated, it could be said that because the material was not confusing or difficult, there were more resources available to try to manage intrinsic load, which, from the participants’ perspective, meant that the material was more understandable. This is not to say that germane load, as presented in this study, represents the effort to understand the material. That effort can be seen in the relationship with self-regulation and germane load, showing that the characteristics of motivation for learners allowed them to generate the effort to better understand it. This falls in line with Schnotz (2010) [38] and Kalyuga (2011) [31]. Furthermore, empirical evidence supports this notion [35], showing that the effort attributed to self-regulation allowed learners to overcome high levels of intrinsic load and generate higher levels of germane load within learner-controlled environments. Finally, the positive relationship between self-regulation and germane load might be explained through the S2D2 framework, which suggests that effort directed at desirable difficulties ultimately leads to better learning outcomes [53]. It is likely that the germane load was directed at overcoming the intrinsic difficulty of the learning material, so a positive correlation with effort management, or as referred to in this study, self-regulation, should be expected.

One possible explanation for the observed discrepancies in the hypotheses regarding intrinsic and extraneous cognitive load and the confirmation of all hypotheses relating to germane load is that learners in learner-controlled environments may have adopted specific strategies that effectively converted much of their cognitive load from intrinsic and extraneous to germane [9], thereby freeing up cognitive resources. This optimization of cognitive load could have enabled learners to better leverage their self-regulation abilities to take advantage of the available cognitive resources, deepening their understanding of the material. All learners, regardless of their expertise or self-regulation, were able to successfully mitigate some of the negative effects of cognitive load in the learner-controlled environment.

The research findings are of value for researchers and practitioners, as they provide insights into how prior knowledge and self-regulated learning skills interact with different types of cognitive load and to what extent these factors matter for learning outcomes. Regarding practical implications, the correlations between the variables underline the importance of providing learners with options to control their learning, taking into account their prior knowledge and level of self-regulated learning skills. Another significant finding of this study implies that it becomes even more essential to focus on fostering germane load within learner-controlled environments, as germane load not only leads to better performance but also helps decrease the negative side of some of the cognitive load effects. Our study offers a unique and original contribution by demonstrating how low levels of intrinsic and extraneous cognitive load can enhance comprehension in learner-controlled environments. Our findings underscore the importance of fostering germane load to improve learning outcomes, highlighting the value of learner control in effectively mitigating cognitive load and promoting deeper understanding.

The current study has some limitations. The most important one concerns the measurement tools used for the current study, which rely on self-reporting of both self-regulated learning skills and cognitive load that might distort the results. It may be that the wording of the items in Leppink et al. (2013) [30] led the participants to confound the concepts. Items address intrinsic load as complex concepts and extraneous load as unclear explanation, thus potentially leading students to be unable to distinguish between the two; unclear explanations in their mind potentially meant the concepts being explained were complex. This is a possibility when subjective measures are used and was discussed by Cierniak et al. (2009) [54], who concluded that the distinction between load types related to subjective cognitive load measures needs clarification. Concerning the germane load items, it would be logical to conclude that high levels of germane load represent high levels of understanding, which has been postulated by previous research [12]. The germane load items used in this study are specifically worded as the material enhancing “my understanding”. As for the self-regulated learning measurement tool, firstly, this quantitative approach is likely to measure the number of times a specific self-regulation strategy was used, not the quality of the learning approach [55]. Secondly, it is suggested by previous research that self-reported questionnaires tend to better reflect overall metacognitive skills rather than the use of specific strategies at a micro-level [56]. Other limitations include the narrow scope of the research conditioned by the small sample, and restrictions imposed by the legal domain might not allow scaling of the research findings. Finally, it would be of high interest to explore causal relations of the variables as well as to understand specific differences between learner-controlled and instructor-controlled environments; however, this research does not provide these insights due to the research design.

Future research should take into consideration these effects when choosing the measurement tool. It should also compare different conditions as well as look into specific aspects of learner control and its impact on different types of cognitive load. It would also be interesting to explore which specific strategies are employed in learner-controlled environments that make students’ mental effort germane and what other factors arise within learner control that could assist students in mitigating cognitive load effects.

7. Conclusions

The present study aimed at discovering the relationships between individual characteristics of students, such as prior knowledge and self-regulated learning skills and different types of cognitive load and understanding, and whether they have significant correlations with learning outcomes in learner-controlled environments. The results show that in learner-controlled environments, both prior knowledge and self-regulated learning skills are significantly correlated with germane load, which has a positive relationship with performance. This implies that when learners with little prior knowledge and low self-regulated learning skills face the absence of guidance and structure, they might not be able to use efficient learning strategies and struggle with increased cognitive load. This underscores the importance of considering the interplay between prior knowledge, learner control, and cognitive load in the design of effective learner-controlled environments, taking into account students’ individual characteristics.