Journal of Computer Assisted Learning

Highly interactive and complex content within e‐learning induces high levels of intrinsic load. Self‐regulated effort represents one strategy that may help learners overcome such issues within e‐learning. Using intrinsic load items representative of content complexity, germane load items representative of learning, and self‐regulated effort items representative of effort, this study analysed survey responses from a group of university students (n = 1,471) who participated in e‐learning courses in South Korea. The results showed that intrinsic load had a negative relationship with germane load, self‐regulated effort had a positive relationship with germane load, and intrinsic load positively moderated the relationship between self‐regulated effort and germane load. These results add to what current research states about the ability to overcome the processing of complicated content through higher levels of student effort.

Lay Description

What is currently known about the subject matter:

High levels of effort lead to learning.
More complex contents often lead to less learning.
Currently, there is no clear answer to how challenging contents changes the relationship between effort and learning.

What the paper adds to this:

Effort and content complexity interact together in a way that impacts learning significantly.
This paper shows that in situations where content complexity is high, learners who try hard will be able to overcome this difficulty.

The implications of this study's findings for practitioners:

This research shows the important for practitioners (instructors) in the calibration of their contents.
Also, it shows that practitioners (instructors) must seek to understand their learners in the creation of their teaching materials.

1 INTRODUCTION

Due to its autonomous nature where students often control the pace at which they learn, online learning magnifies the need for the use of specific effort‐inducing learning strategies that lead to positive outcomes when faced with complicated material (Barnard, Lan, To, Paton, & Lai, 2009; Cunningham & Billingsley, 2003; Jung, 2001; McManus, 2000). Self‐regulated learning strategies are ideal for online learning environments, as effort involved in self‐regulated learning contributes to the process whereby students independently and proactively engage with the content by using self‐motivational and behavioural techniques as a means of accomplishing goals (Zimmerman, 2008). Strategies often utilized by self‐regulated learners include goal setting and planning, self‐evaluating, self‐monitoring, as well as organizing and transforming (Zimmerman & Pons, 1986). One way to ensure such goal‐commitment strategies can be maintained is through the use of self‐regulated effort, a separate technique under the umbrella of self‐regulated learning (Pintrich, Smith, Garcia, & McKeachie, 1991). Self‐regulated effort refers to how committed and dedicated students are when it comes to dealing with particular instructional tasks that may be perceived as difficult to manage within the learning environment (Duncan & McKeachie, 2005; Pintrich et al., 1991).

From an instructional perspective, e‐learning is increasingly challenging learners with the delivery of content consisting of highly interactive elements that are associated with complex tasks that require high levels of cognitive processing Van Merriënboer & Ayres, 2005. When faced with such situations, students experience higher levels of intrinsic cognitive load. Intrinsic load represents the complexity of the content based on the prior knowledge of the individuals who are processing it (De Jong, 2010; Sweller & Chandler, 1994; Sweller, Van Merriënboer, & Paas, 1998; Van Merriënboer & Ayres, 2005). Although intrinsic load is influenced by the learner's prior knowledge of the content being delivered, the interaction between information elements known as element interactivity ultimately determines levels of complexity experienced by the learner (Van Merriënboer & Sweller, 2005). Low element interactivity is when isolated bits of information that can be understood on their own are presented to the students, whereas high element interactivity requires connecting separate bits of information in order to fully understand a specific concept (Paas, Renkl, & Sweller, 2003; Van Merriënboer & Sweller, 2005). Instructional tasks that contain high element interactivity have been associated with higher levels of intrinsic load (van Merriënboer & Sweller, 2010).

Learning that occurs within online environments can be explained through germane cognitive load. Unlike intrinsic load, high levels of germane load are viewed as beneficial to an ideal learning environment due to its connection to learning (Cierniak, Scheiter, & Gerjets, 2009). Germane load levels are determined by specific amounts of effort exerted by learners in order to comprehend the content delivered to them by their instructors (Homer, Plass, & Blake, 2008; Shadiev, Hwang, Huang, & Liu, 2015). When student effort successfully manages content complexity, represented by intrinsic load, learners can effectively construct schema that contributes to their understanding of the content and ultimately learning (Van Merriënboer & Sweller, 2010). It is therefore a point of interest to examine whether or not self‐regulated effort is effective in overcoming high levels of intrinsic load.

2 THEORETICAL BACKGROUND

2.1 The relationship between intrinsic load and germane load

Cognitive load theory is concerned with understanding how learners transfer information from their working memory into their long‐term memory (Cierniak et al., 2009). As there is a limitation of how much information the working memory can process at a given time, understanding how different aspects of cognitive load interact and affect each other is important. Within cognitive load, there are three focal elements: intrinsic load, extraneous load, and germane load (De Jong, 2010; Sweller et al., 1998). Intrinsic load is a combination of the complexity of the content and students' prior knowledge of the content. Extraneous load represents instruction that is superfluous to the point where it distracts the learners. Germane load represents student schema construction, and reflection, which directly contribute to learning (Kolfschoten, Lukosch, Verbraeck, Valentin, & de Vreede, 2010; Sweller et al., 1998). It should be noted that there has been recent debate among some cognitive load researchers regarding the actual existence of germane load. Leppink, Paas, Van Gog, Van der Vleuten, and Van Merriënboer (2014), for example, make the claim that germane load may actually be a representation of intrinsic load rather than effort associated with learning gains, whereas others have refuted this point by showing that effort contributes to the internal consistency of germane load (Debue & Van De Leemput, 2014).

Intrinsic load is conceptually linked to germane load in that varying levels of interacting elements that make up the complexity of the material, represented by intrinsic load, either hamper or contribute to learners' abilities to form schema and store information to their long‐term memory in order to enhance understanding of the content, represented by germane load (Van Merriënboer & Sweller, 2010). The concept of element interactivity can be understood by an example provided by Paas et al. (2003). Referencing the process of how to use a photo‐editing program, they explain that each photo‐editing function key can be learned independently and rather easily. This would be considered low element interactivity because the function key's function represents an isolated element. However, editing a photograph requires understanding multiple elements that interact with each other, such as colour tones, brightness, and contrast. This would be considered high element interactivity because the learner needs to know how each photo‐editing element works with the other elements in order to effectively edit the photo. It is during this high element interactivity phase where intrinsic load increases and negatively affects the transfer of information (multiple photo‐editing elements in this case) into the long‐term memory. Although a certain amount of element interactivity is needed to connect new information to already existing schema and eventually contribute to germane load (van Merriënboer & Sweller, 2010), if the gap between what a student knows and what a student needs to learn creates excessive cognitive processing when dealing with element interactivity, then learning can be negatively affected (Van Merriënboer & Sweller, 2010). In other words, germane load decreases when complex content that is significantly beyond a student's comprehension level is presented within a learning environment. One solution to enhance the likelihood of learning would be to appropriately align the complexity of the content with the students' level of prior knowledge (De Jong, 2010). However, because of massive enrollment often associated with e‐learning, this may be unrealistic.

Although some research suggests that intrinsic load cannot be altered through instructional manipulations (Ayres, 2006; Paas et al., 2003), other research claims that intrinsic load can be controlled by the way in which complicated content is presented (Gerjets, Scheiter, & Catrambone, 2004; Pollock, Chandler, & Sweller, 2002; Van Merriënboer, Kirschner, & Kester, 2003). It is important to note that the actual content cannot be changed to lower intrinsic load, as it still needs to be presented in its entirety (Van Merriënboer & Sweller, 2010). However, sequencing the content from low interactive elements in the early stages of a lecture to high interactive elements towards the latter stages of the lecture allows students to receive the full content, while processing it in a simple to complex manner (Gerjets et al., 2004; Pollock et al., 2002). In other words, presenting the information from simple to complex allows students to effectively connect new information to previously formed schema without overloading their cognitive processing. By the time they are presented with high interactive elements at the end of the lesson, new schema has already been formed to allow them to connect all of the information (Van Merriënboer & Sweller, 2010). In theory, presenting content in this manner should increase levels of germane load through the reduction of intrinsic load.

Empirical research shows the difference of content comprehension between high intrinsic load and low intrinsic load. Pollock et al. (2002) presented two scenarios: a high intrinsic load situation where content containing the full set of interacting elements was presented all at once and a low intrinsic load situation where isolated elements were initially presented followed by the full presentation of the interactive elements. Results showed that when intrinsic load was lowered by sequencing the element interactivity, higher levels of understanding of the content occurred. Gerjets et al. (2004) also compared high and low intrinsic load environments. High intrinsic load was represented by an explanation of the overall problem procedure, whereas low intrinsic load was represented by breaking down the solution procedures into more manageable separate parts. Their results showed that although both groups received the same content, better problem solving occurred in the low intrinsic load environment due to the sequencing of the content. It is, therefore, apparent that although the content cannot be altered, the way in which the content is presented can lower the students' perceived complexity of the content and ultimately increase their understanding.

2.2 The relationship between student effort and germane load

Germane load is reflective of learning in that motivational effort used to construct schema has a positive effect on content comprehension (De Jong, 2010). Such effort provided through self‐regulation strategies, specifically, has been shown to be a key determinant of success within online learning environments (Puzziferro, 2008; Saw, 2011; Shea & Bidjerano, 2012). For example, instructional environments that encourage self‐regulation have not only been shown to lead to more effort and persistence but have also been shown to lead to higher levels of academic achievement (Komarraju & Nadler, 2013; Ley & Young, 2001; Pintrich et al., 1991). Specifically, it has been shown that the use of self‐regulated learning strategies involving planning within multimedia online learning environments leads to greater learning (Moos, 2013; Moos & Azevedo, 2008). Additionally, self‐regulated learning processes involving memorization, elaboration, and organization have been attributed to higher levels of learning within online learning environments. Showing a direct relationship between self‐regulated effort and learning specifically, Puzziferro (2008) found that students who reported the use of higher levels of self‐regulated effort to manage academic tasks within a university e‐learning course received higher grades than those who reported lower levels of self‐regulated learning within the same course.

2.3 Overcoming intrinsic load through student effort

It is commonly thought that students who are highly motivated and put a lot of effort into their studies can more easily overcome difficulties or obstacles they may come across within an e‐learning environment and eventually succeed in their learning. One way of understanding student characteristics and behaviour in online learning contexts is self‐regulated learning. Self‐regulation is a combination of various student factors, but combined together, learners with high levels of self‐regulation do not engage in behaviour that will distract them from the learning process and are assiduous in the face of challenging tasks (Dabbagh & Kitsantas, 2012). Furthermore, when faced with a challenging task, learners with high levels of self‐regulation will tend to engage more deeply with the task and therefore develop a deeper understanding of the material (Perry, Phillips, & Hutchinson, 2006). Also, individuals who self‐regulate their behaviour generally do not blame the external environment for failures of learning and tend to hold themselves accountable in such situations (Zimmerman, 1990). The effort that is one part of self‐regulation behaviours can specifically be used to overcome materials or content that is very challenging for the student, which may lead to greater success in their course work and greater learning (Perry et al., 2006). An example of this is that learners with high levels of self‐regulation will put in effort and try to succeed even in the face of material that may seem to be incomprehensible to them at first (Gerjets, Scheiter, & Tack, 2000; Leutner, Leopold, & Sumfleth, 2009; Moos, 2011; Zimmerman, 1990). Therefore, self‐regulated learning strategies may be used in order to deal with materials and contents that may be beyond a learner's current level of comprehension.

Previous research has shown that in spite of difficult contents or challenging instruction, some types of learners will perform better than others. This is due to the fact that high levels of self‐regulation will lead to adaptive strategies that contribute to academic success, even within challenging learning situations (Joo, Bong, & Choi, 2000; Komarraju & Nadler, 2013; Linnenbrink & Pintrich, 2002; Lynch & Dembo, 2004; Saw, 2011; Wang & Newlin, 2002). More specifically, the process whereby self‐regulated learners feel accountable for the knowledge they acquire leads to more success within e‐learning environments (Kosnin, 2007). Relative content that is difficult for the learner can induce a degree of cognitive demand that may be detrimental for learning; however, self‐regulation may help to mitigate this challenge (Lane & Lane, 2001). In situations where there is a gap between the learner's current knowledge level and the knowledge level they should have to complete the task, self‐regulated effort can help lead students to make adjustments to their learning as needed (Perry & Winne, 2006; Winne, 2001), which should then lead to greater levels of learning in the e‐learning environment (Moos & Azevedo, 2008; Van Gog, Kester, & Paas, 2011).

Although no research was found that has investigated the ability of students to overcome complicated content through the use of self‐regulated effort as a specific strategy, empirical research does support the notion that the effort involved in self‐regulated learning strategies in general can help students succeed, even when both content complexity and lack of knowledge of the topic play a role. An example of this is when students face complicated expository text within an online learning environment, self‐regulated visualizing techniques used to construct a mental image of the content leads to deeper mental processing in addition to increased levels of comprehension and ultimately learning (Leutner et al., 2009). Additionally, the use of self‐regulated monitoring strategies within a multimedia e‐learning context has been shown to lead to success when students face difficulty within the learning environment. This is evident in that Moos (2011) was able to show that students who received no feedback were able to outperform students who did, due to the fact that the former showed higher levels of effort through self‐regulated monitoring strategies.

2.4 The current study

This study draws its participants from the Open Cyber University (OCU) in South Korea. The OCU is a network of brick‐and‐mortar universities that provide funding and management for online classes (Jung & Rha, 2001). The students were traditional students who studied offline at one of the universities but supplemented their standard university experience with some online classes from the OCU. Students at the OCU are given full credit and take advantage of approximately 400 different classes that are offered each year to approximately 120,000 students (About OCU, n.d.). The member universities provide direction, design, content, and professors for the classes, with a group of delegates selected from the member universities forming a council tasked with the mission to guide the policy and pedagogy of the OCU (Jung & Rha, 2001). Maximizing student learning by allowing learners to create effective and useful cognitive schema should be one of the main goals of any online learning environment.

Levels of content complexity as represented by intrinsic cognitive load have been shown to have a negative impact on learners' levels of germane load (De Jong, 2010; Paas et al., 2003; Pollock et al., 2002; Sweller et al., 1998; van Merriënboer & Sweller, 2010). Application of effort through self‐regulated learning strategies has been shown to benefit learner's levels of learning (Komarraju & Nadler, 2013; Ley & Young, 2001; Moos, 2013; Moos & Azevedo, 2008; Pintrich et al., 1991; Saw, 2011; Shea & Bidjerano, 2012; Zimmerman, 2008; Zimmerman & Pons, 1986). Self‐regulated learning specifically has been shown to be an effective strategy that allows students to maintain their focus and study in difficult learning environments and has also been shown to lead to higher levels of achievement among online university learners (Pintrich et al., 1991). It stands to reason then that self‐regulated effort may interact with intrinsic load in a manner that allows learners to overcome difficult contents and still learn. Extant research has looked at these relationships as part of specific aspects of e‐learning environments, taking place at specific moments of instruction. This study, however, looks at the bigger picture by examining the relationships in a general sense of an online lecture. Doing so should provide a broader interpretation of what is generally occurring between intrinsic load and germane load, self‐regulated effort and germane load, as well as the possible moderating effect of self‐regulated effort on the relationship between intrinsic load and germane load. Therefore, the current study examines the following hypotheses.

2.5 Research hypotheses

Intrinsic load is negatively correlated with germane load.
Self‐regulated effort is positively correlated with germane load.
Intrinsic load moderates the relationship between self‐regulated effort and germane load.

3 METHODS

3.1 Research procedures and data collection

The first step in this research project was a set of broad qualitative interviews with 10 students who had taken classes in the OCU. The questions asked were not particularly focused but did revolve around how the students felt about the nature of instruction and student‐to‐student interaction in the OCU classes they participated in. It was discovered that the OCU did not have a lot of student‐to‐student interaction and that classes tended to be focused around video lectures and solving quizzes. This qualitative component allowed the researchers to start focusing in on the factors that were more important within the OCU. More specifically, that the students' cognitive processing of and engagement with course materials such as videos and PDFs were the most critical aspect of learning in the OCU. There were many comments in the qualitative component of this research in regard to the passive nature of learning and the difficulty in maintaining motivation when dealing with difficult materials. To investigate the issues more thoroughly, a larger, more specific survey was designed using cognitive load as a lens to understand what was happening in the OCU. This survey would seek to discover how students could possibly overcome the more difficult classes and learning situations they might face. It is data gleaned from this larger survey that will be used in the results presented here.

The survey was first written in English and then translated into the language of the OCU, which is Korean. To establish the accuracy and reliability of the survey, a specialist in both online learning and the OCU looked at the survey's translated items and verified them as an accurate and reliable representation of the English items. From there, the translated items were put into a Google © sheets form, a link to which was sent to the main administrative offices of the OCU. The OCU administration verified that the survey overall and each of its items were appropriate. A link to the survey was then posted on the main administrative board of the OCU, inviting students to participate. Students who visited the main administrative board then filled out the survey, which generated a random representative sample of the population of the OCU (Lange, Costley, & Han, 2017).

3.2 Participants

The subjects who chose to participate in this research answered questions in a survey about their OCU experience, and 1,694 valid surveys were submitted. After receiving the data file, the next step in the study was the removal of outliers. Linear regression of intrinsic load and self‐regulated effort onto germane load was used to generate Mahalanobis, Cook's, and Leverage values to look for outliers. Any subjects whose results met the standard for two or more of these tests were removed from the analysis, leaving 1,471. A brief analysis of the outliers showed there were no shared traits among the discarded participants. The following results and tables subsequent to this are generated from these 1,471 remaining subjects. Of the remaining 1,471 subjects, 755 (51%) were female and 716 (49%) were male. The oldest participant was 63 and the youngest was 15, the average age was 23.3, with a standard deviation of 3.2. The demographic distribution of participants in this research is similar to other research into e‐learning environments in South Korea (Suh & Kim, 2013).

3.3 Instrument development

To develop both the germane load and intrinsic load measurement, item from Leppink, Paas, Van der Vleuten, Van Gog, and Van Merriënboer's (2013), The development of an instrument for measuring cognitive load, was used. Leppink et al.'s (2013) paper gives descriptions of the three main types of cognitive load (intrinsic, extraneous, and germane) and a method to measure them in a survey. A total of seven items were used in the present study, three for intrinsic load and four for germane load. Leppink et al. (2013) used exploratory factor analysis to explain how aspects of cognitive load relate to each other. The fact that the loadings for three cognitive load constructs independently represented a robust factor not only supports the triarchic theory of cognitive load but also provides justification for measuring different aspects of cognitive load separately (Deleeuw & Mayer, 2008). The items used for the germane load construct were as follows: The lecture really enhanced my understanding of the topic, the lecture really enhanced my knowledge and understanding of the of the class subject, the lecture really enhanced my understanding of the concepts associated with the class subject, and the lecture really enhanced my understanding of concepts and definitions. The items used for the intrinsic load construct were as follows: The topics covered in the lecture were very complex, the lectures covered information that I perceived as very complex, and the lectures covered concepts and definitions that I perceived as very complex. There was a small change in the wording of the items between the present research and Leppink et al.'s (2013) study, with the word “activity” being replaced with the word “lecture.” This was done in accordance to Leppink et al.'s (2013) claim that rewording text to match the context of a specific study is an acceptable modification to the items. All of the cognitive load items that were used were measured using a Likert‐type scale ranging from 0 to 10, with 0 being “strongly disagree” and 10 being “strongly agree.” The Cronbach's α for the germane load construct was .961, and the Cronbach's α for the intrinsic load construct was .919, both of which were high enough to be used in this type of research.

To generate the self‐regulated effort construct, items from Motivated Strategies for Learning Questionnaire (MSLQ) were adapted for use. The MSLQ (Pintrich et al., 1991) is designed to test varied aspects of a student's use of learning strategies and motivational orientations. The aspect of the MSLQ used in this study, self‐regulated effort, is made up of four items: I often lose focus when I study so I quit before I finish what I planned to do (reversed); I work to do well at school even if I get confused; when coursework is unclear, I give up or only study the easy parts (reversed); and even when study materials are complex, I manage to keep working until I finish. The Likert‐type scale used for these items was set at a range from 0 to 10, with 0 representing “disagree” and 10 representing “agree.” Although the MSLQ was originally designed to be set at a 7‐point Likert‐type scale, the current study set it at 0 to 10 to ensure consistency with the range throughout the entire survey. This was done because the main constructs used in this study are the cognitive load constructs, which are set at a 0 to 10 range. Justification for altering the range is provided by various studies (Ergul, 2004; Nie, Lau, & Liau, 2011; Simon, Aulls, Dedic, Hubbard, & Hall, 2015). Additionally, the wording of the items was slightly modified to represent the context of this study. This is also justified by numerous studies (Ergul, 2004; Johnson & Sinatra, 2013; Lawanto, 2009; Lynch & Dembo, 2004; Nie et al., 2011; Phan, 2013; Simon et al., 2015; Wolters, Pintrich, & Karabenick, 2005). The Cronbach's α for self‐regulated effort was .727, which though lower than intrinsic load and germane load, is still appropriate for this type of research.

4 RESULTS

Table 1 shows the means, standard deviations, and Pearson correlation coefficients of the main variables used in this study. The findings showed that intrinsic load is negatively and significantly associated with self‐regulated effort (β = −.292, p < .01). Intrinsic load was also negatively and significantly correlated with germane load (β = −.644, p = <.01). Finally, self‐regulated effort was positively and significantly related with germane load (β = .439, p = <.01). The mean for self‐regulated effort was 6.81, with a standard deviation of 1.59. The mean for germane load was 6.49, with a standard deviation of 1.99. Intrinsic load was significantly lower than both self‐regulated effort and germane load with a mean of 3.93 and a standard deviation of 2.37.

Table 1. Descriptive statistics and Pearson correlation coefficients of the main variables (n = 1,471)

	Mean	SD	Self‐regulated effort	Intrinsic load	Germane load
Self‐regulated effort	6.81	1.59	1
Intrinsic load	3.93	2.37	−.292**	1
Germane load	6.49	1.99	.644**	−.439**	1

** Correlation is significant at the .01 level.

Using linear regression, both intrinsic load and self‐regulated effort's effect on germane load were measured. The overall model had strong predictive power in relation to germane load (r² = .484). Also, each one unit increase in the intrinsic load scale lead to a .23 (p = <.001) decrease in germane load. Conversely, for every one unit increase in self‐regulated effort, there was a .71 (p = <.001) increase in germane load.

PROCESS macro (Model 1, Hayes, 2013) was used to test the interaction effect of self‐regulated effort and intrinsic load on germane load, as is shown in Figure 1. To show this, 10,000 bootstrap samples were used with a 95% confidence interval. Also, variables were mean centred to +/−1 standard deviation, which showed strong evidence of an interaction effect based on a standardized coefficient. Intrinsic load positively moderated the effect between self‐regulated effort and germane load, or in other words, as intrinsic load increases, the strength of the relationship between self‐regulated effort and germane load also moderately increases.

**Figure 1**
Open in figure viewer PowerPoint

The moderating effects of intrinsic load on the relationship between self‐regulated effort and germane load [Colour figure can be viewed at wileyonlinelibrary.com]

To measure the effect of moderation, PROCESS macro was used to centre the variables and measure the relative effect of self‐regulated effort on germane load at the average level of intrinsic load and at one standard deviation above and below the mean. This created low, average, and high groupings of relationships. In all conditions, there was a statistically significant relationship between self‐regulated effort and germane load. However, as can be seen in Table 2, in the low intrinsic load condition, the effect size (.68) is weaker than in the average intrinsic load condition (.71) and weaker again than in the high intrinsic load condition (.73). As can be seen in Figure 2, this creates an effect where the lines come closer together, whereby the low intrinsic load condition has a flatter line than the average intrinsic load condition and the high intrinsic load condition is slightly steeper than both.

Table 2. Centred effects for self‐regulated effort on germane load at each level of intrinsic load (n = 1,471)

	IL	b	t	p
Low intrinsic load	−2.38	.68	23.01	.000
Average intrinsic load	0.00	.71	27.44	.000
High intrinsic load	2.38	.73	17.55	.000

**Figure 2**
Open in figure viewer PowerPoint

The moderation effect of intrinsic load on self‐regulated effort [Colour figure can be viewed at wileyonlinelibrary.com]

5 DISCUSSION

The analysis of survey data from university students taking e‐learning courses in South Korea gave insight into the relationships between content complexity, student effort, and learning. Using intrinsic load as a measurement of content complexity, self‐regulated effort as a measure of student effort, and germane load as a measure of learning, this study concluded the following: Intrinsic load was negatively correlated with germane load, self‐regulated effort was positively correlated with germane load, and intrinsic load positively moderated the relationship between self‐regulated effort and germane load. The negative relationship that was found in this research between intrinsic load and germane load is reflective of research that shows that difficult content can negatively affect learning outcomes (Gerjets et al., 2004; Pollock et al., 2002). The high levels of element interactivity often associated with intrinsic load, lead to lower levels of germane load, as was found in the current study (De Jong, 2010; Paas et al., 2003; Pollock et al., 2002; Sweller et al., 1998; Van Merriënboer & Sweller, 2010). This effect occurs in situations where the contents are too difficult for the learner to process from their working memory to their long‐term memory (De Jong, 2010; Paas et al., 2003; Sweller et al., 1998; Van Merriënboer & Sweller, 2010). The positive relationship between self‐regulated effort and germane load is also supported by research involving the general use of self‐regulated learning strategies to enhance learning (Joo et al., 2000; Kosnin, 2007; Lane & Lane, 2001; Lynch & Dembo, 2004; Saw, 2011; Van Gog et al., 2011; Wang & Newlin, 2002), in addition to research showing a direct relationship between self‐regulated effort and learning specifically (Puzziferro, 2008). Theorists argue that this effect may be caused by increased student focus and application of various learning strategies, which may account for the moderated effect of intrinsic load, as students with high levels of effort are able to overcome more challenging learning situations (Gerjets et al., 2000; Leutner et al., 2009; Moos, 2011).

Some research suggests that some levels of intrinsic load are necessary for learning and may contribute to developing the learner's schema (Van Merriënboer & Sweller, 2010); however, this study found that this was not the case. Increases in intrinsic load led to corresponding decreases in germane load. This means that the students felt that they learnt more from classes that were easier for them. However, this research goes some way to refuting a study by Leppink et al. (2014), which indicates that germane load may simply be a representation of intrinsic load as opposed to the invested effort that may contribute to learning. In this study, the positive relationship between self‐regulated effort and germane load was stronger than the negative relationship between intrinsic load and germane load. Furthermore, self‐regulated effort combines with intrinsic load to increase germane load. These two findings suggest that germane load is not simply a representation of intrinsic load, as has also been suggested by Debue and Van De Leemput (2014).

This aside, the negative relationship between intrinsic load and germane load could be a problem in online learning situations as class sizes and instructor‐to‐learner distance make it challenging for instructors to calibrate content complexity with their students' levels. The current study supports what some research claims in regard to the effects of student effort on the relationship between content complexity and learning. Research suggests that students with high levels of self‐regulation will be more focused on the class contents, so as material gets more difficult, they should perform better than the students with lower levels of self‐regulation (Dabbagh & Kitsantas, 2012). Another way in which learners with high levels of self‐regulation may perform better in more challenging environments is their tendency not to blame the environment for their problems and their willingness to exert effort even in cases where the material seems challenging (Gerjets et al., 2000; Leutner et al., 2009; Moos, 2011; Zimmerman, 1990). These strategies may have contributed to the results found in this study in which the relationship between levels of self‐regulation and learning was highest in situations of higher content difficulty. Lane and Lane (2001) have shown that self‐regulation allows learners to cope with some cognitive demands, and the current study adds to this by showing that self‐regulation also allows learners to cope with intrinsic load. Furthermore, if there is too great a gap between learners' knowledge and the level of contents, then the learners may struggle to retain the contents. Self‐regulation also promotes the use of content visualization and monitoring (Gerjets et al., 2000; Gerjets & Scheiter, 2003; Leutner et al., 2009; Lynch & Dembo, 2004; Moos & Azevedo, 2008; Perry & Winne, 2006; Van Gog et al., 2011), which can overcome this challenge as has been shown in the current study.

6 CONCLUSION

Understanding the relationships among intrinsic load, student effort, and germane load is important as the distance and lack of direct instructor presence can place greater cognitive demands on the learners, specifically within e‐learning environments where students are often challenged with complex content represented by diverse forms of media (Cunningham & Billingsley, 2003; Jung, 2001; Mayer & Moreno, 2003; McManus, 2000). E‐learning research involving cognitive load and effort exerted through self‐regulation has shown that when students show higher levels of effort within specific situations during specific points in an online lecture, they are able to compensate for difficult instruction. The results of the current study add to existing research in that in a more general sense, effort has been shown to be a useful catalyst for overcoming difficult content, showing the effectiveness of self‐regulation over the course of a lecture rather than during a specific part of the lecture. Additionally, this study highlights the need for instructors to calibrate tasks to the levels of their learners and for learners to engage in self‐regulation learning strategies to maximize what is learned. Although the results of this study contribute to discourse of online learning research through evidence that student effort can overcome complex content over the course of a lecture, there are some limitations. This study quantitatively examined relationships involving intrinsic load, self‐regulated effort, and germane load through survey analysis. However, a qualitative approach may provide further explanation as to the reasoning behind these results. Also, this study used subjective measurements involving self‐regulated effort and elements of cognitive load. Although Leppink et al.'s (2013) research showed the loadings for the subjective measurements to be robust, designing controlled environmental conditions that manipulate element interactivity and measure objective learning scores may strengthen the current results. Despite these limitations, the results provide valuable insight into the effects of intrinsic load on the relationship between student effort and learning.

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Volume34, Issue6

December 2018

Pages 652-660