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ISSN : 2092-674X (Print)
ISSN : 2092-6758 (Online)
Asia-Pacific Collaborative education Journal Vol.6 No.2 pp.15-33

The effects of learning management system quality and learner’s characteristics regarding scholastic performance

Jong-Ki Lee
Jong-Ki Lee, Ph.D. is an Invited Professor in Innovation Center for Engineering Education, College of Engineering at ChungNam National University (CNU), Korea, from 2010. He received his Ph.D. in management information systems from Daegu University, Korea, in 2005. He was a research professor of center for BK21 in school of business administration at Kyungpook National University, Korea, from 2006 to 2009. His research interests include creativity-based Instruction, management Innovation, and learning performance with u-technology and learning community.
Received Date: Oct. 2, 2010, Revision received Date: Dec. 7, 2010, Accepted Date: Dec. 10, 2010


This study presents a researchmethodology based on a successfule-learning model, which presents theinter-relationship between an e-learner’scharacteristics and quality perception inregard to LMS (learning managementsystems). This research model focuses onthe e-Learner’s cognitive empathy,self-regulatory efficacy, self-regulatedlearning strategies, and satisfaction withthe learning environment. The learningenvironment consists of a learningmanagement system, learning content, aswell as interactions which were providedthrough the e-learning procedures.Based on the assumption thate-learning, as information system - itsattributes, correlates with the learner’scharacteristics, the study verifieduser-satisfaction and system’s quality toinformation systems. In the informationsystems success model, independentvariables are such as an e-Learner’scognitive empathy, self-regulated learningstrategy, self-regulatory efficacy,satisfaction regarding learning content,system quality, and interactions betweenthe teacher and learner; and thedependent variables are such as thee-Learner’s expected performance andactual performance levels throughprofessor evaluations.This paper concludes that thee-learner’s characteristics, such ascognitive empathy and self-regulatedlearning strategies, are considered asimportant variables in respect toe-learning performance. Additionally, LMSquality is also considered to be important.The validity of this research model will bedemonstrated on an empirical basis.



 Academic achievement is defined as an assessment of the learner’s e-learning environment, while self-study ability is defined as an assessment of the learner’s aptitude regarding his or her self-studies. This approach reveals both extensive and effective trends resulting from an e-learning research model. In addition, many researchers hold differing opinions as to various views regarding educational engineering and information systems. They have focused on a previous exploratory study regarding explanations of variations in e-learning effectiveness (Wang, 2003).

 In addition to, the tendency in educational engineering to introduce theoretical variables which explain e-learning effectiveness is insufficient, with the exception of a few selected information systems (Piccoli et al., 2001). Moreover, this tendency, which combines information systems and educational engineering, is rarely observed.

 The body of this research investigates the theoretical background regarding e-learning pedagogies. It closely examines the relationship between information systems success models by Delone & Mclean (2003) and e-learning. This research suggests and also verifies new research models which assess or evaluate e-learning effectiveness based on models of educational engineering variables and information systems, each of which are verified empirically.

 The purpose of the present research emphasizes the fact that cognitive empathy, self-regulatory efficacy, and self-regulated learning strategy are important variables relating to the e-learner’s scholastic performance.

Literature Background


 The variables for success of e-learning course are related to the learning management systems (LMS) and learners’ characteristics. Especially, performance of learning consists of learner’s self-regulatory efficacy and empathy.

 Empathy is defined as relating to another person’s private world in a similar fashion as to how one feel about one’s own (Roger, 1961). Empathy is considered to be an essential component for general human relations, providing benefits regarding interpersonal relations as follows:

 First, empathy improves people’s understanding of others and increases the odds of forecasting on their behalf (Bylund & Makoul, 2005). Second, empathy creates a positive atmosphere, fostering respect and acceptance, rather than an attack in regard to conflict circumstances. Therefore, it enables conflict dissolution to occur and also strengthens relationships (Guerney, 1977). Third, empathy is considered to be an essential component in regards to pro-social action (Coke et al., 1978). The progress of empathetic ability improves pro-social action and results in the decrease of violent behavior (Davis et al., 1999). For this reason, empathy affects his or her own self behavior in the presence of others, and is a very important component affecting the formation of interpersonal relations because it controls psychological status through understanding other’s vicarious experiences (Spiro, 1992; Preston & de Waal, 2002). The dimensions regarding the construction of cognitive and emotional empathy are summarized in the next two sections.

 Cognitive empathy is defined as role-playing (Stiff et al., 1988). Cognitive empathy is defined as the ability to relate to others’ roles and take an alternate view. It deduces the perception regarding the experience or activity of others. Role-playing in dimension is highly oriented, in that it takes a role from the position and viewpoint of that person (Feshbach, 1987). Cognitive empathy is gained through taking a role from the position and viewpoint of that person, which promotes interpersonal relations through interaction by predicting other’s behaviors, and by doing something as a favor (Sturmer et al., 2005). Therefore, the cognitive empathy process is similar to the interaction process regarding learning and e-learning progression.

 Emotional empathy is a consideration oriented approach toward others, representing compassion, tenderness, and the willingness to agree with others’ experiences, which may disagree with their own (Davis et al., 1999). In regards to e-learning, interaction with other teachers and attendees represents an essential characteristic regarding educational acquisition. Learners with a high level of cognitive empathy can interact on a better with teachers and other learners. They actively participate in the learning process because they have a feeling that the shared learning contents, shared circumstances of the attendees, and shared affection towards learning is important regarding e-learning environments (Lee, 2008).

Self-regulatory efficacy and self-regulated learning strategy

 Self-regulatory efficacy (SRE) is defined as the efficacy of well-performed self-regulatory mechanisms, such as self-observation, self-judgment and self-response (Bandura, 1986). Confidence promotes learning performance through the promotion of individual goals, such as traditional education psychology (Bandura, 1997).

 The learner’s independent assessment of self-regulatory learning ability is called SRE (Bong, 1998). Self-regulatory learning is defined as a learner’s intended effort towards learning subjects (Corno & Mandinach, 1983). It is a systematic management process regarding one’s own thoughts, emotions and behavior, as well as one’s personal goals and achievements (Schunk, 2000).

 In regards to self-regulated learning strategies, Zimmerman & Martinez-Pons (1988) reported on the correlation between self-regulated learning strategies and the learner’s performance in Mathematics and English. As well, Zimmerman, Bandura, & Martinez-Pons (1992) researched the causes and consequences of studying roles in regards to self-efficacy. Zimmerman & Martinez-Pons (1986) developed an integrated strategy, based on a self-regulated learning strategy, including self-testing, organizational transformation, goals and planning, pursuing information, recording and checking, structured environment, strength and self-punishment, demonstration and memory, seeking help, as well as review strategy.

 According to self-regulatory learning principles, the learner utilizes the strategic relationship between self-regulation and learning in order to attain his or her chosen self-learning goal, and to develop, revise, and complement the learning strategy via self-feedback. The learner must make consistent efforts in order to sustain learning motivation (Zimmerman, 1990).

The relationship between e-learners and the information system success model

 As one of those information systems, a LMS is applicable to an information process system that processes learning content, and supports all types of variables related to other learning systems. E-learning content is defined as the product created through LMS (Lee, 2004). The correspondence course of interaction between the teacher and students is equivalent to the human service process that the information system department staff offers to system users (Lee, 2004). In student situations, LMS can be a critical factor in determining e-learner satisfaction through e-learning, since it discharges its transmission duties through a variety of learning contents, and offers a unique format with respect to each lesson (Lee, 2004).

 From an offline perspective, LMS utilizes a similarly logical approach, such as when classroom and educational facilities transfer educational content within a school or private educational organization. This affects learner satisfaction, which is not related to attending lectures in regards to a given subject. Learning contents contain differing qualities, according to each lecturer and producer’s ability or character. Therefore, unlike LMS, the learning content is considered to be a critical assessment factor and is a direct factor in determining learner satisfaction (Lee, 2004). Similar logic dictates as to which factors determine learner satisfaction in regards to offline learning content. Even with available support for solving technical problems, the learner can appeal to the teacher for assistance by using LMS, in addition to receiving guidance and assistance regarding the learning content. Therefore, the teacher’s quality of service has a greater role in comparison to the general information system department staff. The service quality in e-learning compares to an interaction quality in teacher and e-learner.

Research Model and Hypothesis

Research model

 This research was accomplished through on empirical study, in which the research model is illustrated in Figure 1. The research model is based on the modification of an information systems success model (Delone & Mclean, 2003). The information systems success model focuses on user satisfaction and system’s quality to information systems. This research considers an information system’s attributes and e-learner’s characteristics. In addition, it supports education engineering with respect to e-learning. This model is comprised of independent variables, such as an e-Learner’s cognitive empathy, self-regulated learning strategy, self-regulatory efficacy, satisfaction regarding learning content, system quality, and interactions between the teacher and learner. The dependent variables are comprised of the e-Learner’s expected performance and actual performance levels through professor evaluations.

Figure 1. Research Model


 E-learning is regarded as an information system. In addition to, e-learning satisfaction should correlated with the learner, based on his or her characteristics. Based on these points of view, the following hypothesis should be considered.

 According to a consumer behavior theory, satisfaction is measured through a customer’s response regarding fulfillment, as well as customer judgment with respect to products or services. Satisfaction also includes fulfillment in regards to one's performance (Oliver & Swan, 1989). In regards to judgment, fulfillment of one's performance is required as a reference that will be compared to a standard. References are necessary as comparisons with results or outcomes in order to judge satisfaction levels (Au, et al., 2002). From the viewpoint of system satisfaction, system’s user satisfaction is compared to e-learner’s satisfaction. In regards to an information system, consumers or customers of consumer behavior theory refer to users who utilize the system directly, unless utilized it for technical reasons (Au et al., 2002; Delone & Mclean, 1992). In similarity to consumer behavior theory, an end-user’s satisfaction is dependent on the attitude toward a specific computer application system which is utilized (Doll & Torkzadeh, 1988). Satisfaction can also be justified by the perceived or emotional assessment regarding fulfillment levels, referring to expected performance via the information system (Au et al., 2002). E-learning is also regarded as an information system. Therefore e-learning satisfaction should correlate with the end user, based on his or her information system satisfaction and expectation. Based on these points of view, the following hypothesis should be considered:

 Hypothesis-1 (H1): A learner's satisfaction with LMS will be positively related to the e-Learner’s expected performance outcomes.

 With respect to self-regulated learning, the learner uses a strategic relationship between self-regulation and learning in order to reach his or her chosen self-learning goal as well as to develop, revise, and complement the learning strategy via self-feedback. Consequently, there are close to satisfaction and self-regulated learning strategy. The learner must make consistent efforts in order to sustain learning motivation (Zimmerman, 1990).

 A lack of learning strategies is one of the important variables which explain as to why learners have difficulties (Balajthy, 1990). In regards to e-learning, a considerable amount of research has confirmed that issues related to learner acquisition are a key factor regarding academic achievement and satisfaction levels (Lyman, 1998).

 Pintrich (1995) characterizes self-regulated learning as constant adjustment of one’s cognitive activities and processes to the demands of a particular learning situation (Pintrich & Groot, 1990). In the conceptual framework formulated by Pintrich (2000), the self-regulation process consists of four phases, namely forethought; panning and activation; monitoring; control; and reaction and reflection. These phases are not necessarily linearly and temporally ordered, and each phase is characterized by distinctive learner’s activities within four general areas – cognition, motivation, behavior, and context (Bidjerano & David, 2007). E-learning strategy is required for self-directed learning in conjunction with instructional design strategy and goals (Bandura, 1997). Based on these points of view, the following hypothesis should be considered:

 Hypothesis-2 (H2): A learner's self-regulated learning strategy in regards to e-learning will be positively related to the e-Learner’s expected performance outcomes.

 In addition, successful self-regulatory efficacy (SRE) learners will be more concerned with the substance and quality of the learning contents in comparison to unsuccessful self-regulatory efficacy learners. Lower achievement SRE learners will be interested in easily accessible information systems and focused on the understanding. Lower achievement SRE learners will also prefer methods regarding a given learning content (Lee, 2004). According to Thatcher & Pamela (2002), personal innovation in regards to information technology has an effect on computer self-efficacy. Based on these points of view, the following hypothesis should be considered:

 Hypothesis-3 (H3): A learner's self-regulatory efficacy in regards to e-learning will be positively related to the e-Learner’s expected performance outcomes.

 Cognitive empathy is positively related to interpersonal relations, as well as the process of interaction in regards to e-learning, for the reason that cognitive empathy strengthens one’s ability to understand as well as to deal with this ability appropriately. Cognitive empathy is also based on understanding through assuming a role based on the position and viewpoint of others rather than through emotional empathy. Therefore, in regards to e-learning, cognitive empathy is a more important variable because it requires process as well as the ability to understand, and interaction with others is emphasized within the learning processes. Based on these points of view, the following hypothesis should be considered:

 Hypothesis-4 (H4): A learner's cognitive empathy will be positively related to the e-learner’s self-regulatory efficacy.

 The learner’s satisfaction with e-learning and assessment of the information system are compared with consumer behavior theory. Traditionally, in the field of information systems, it has been assumed that a user’s information system satisfaction generated a higher level of performance compared to unsatisfied users (Bailey & Pearson, 1983). For example, according to Gatian (1994), there is a significant relationship among user satisfaction, decision-making performance, and efficiency variables. As well, in regards to the ISS model of Delone & Mclean (1992), satisfaction was noted to be an effective variable regarding working efficiency at the decision-making level. Based on these points of view, the following hypothesis should be considered:

 Hypothesis-5 (H5): A learner's expected performance will be positively related to the e-Learner’s actual performance outcomes.

 According to the ISS model, system quality measures the information system process itself, as well as its effect on user satisfaction (Delone & Mclean, 1992). System quality implies accuracy and efficiency, according to communication theory, that is based on the ISS model (Delone & Mclean, 1992). With regard to information system theory, system quality is based on how easily a user can interact with the system (Doll & Torkzadeh, 1988; Rai et al., 2002). From these points of view, the following hypothesis should be considered:

 Hypothesis-6 (H6): The system quality level contained within a learning management system will be positively related to the e-Learner’s satisfaction level.

 It is acknowledged that system quality within an information systems success model can be substituted for perceived ease-of-use (Rai, et al., 2002; Seddon & Kiew, 1997). Perceived ease-of-use can be explained as the perception regarding the amount of effort that is required using a system, which is an important variable in regards to attitudes toward information systems (Davis, 1989; Davis et al., 1989). Also, according to the revised ISS model, service quality affects user satisfaction (Delone & Mclean, 2003).

 The learning processes created through discussion and by utilizing a messenger program are also important in regards to e-learning. Course learners will share and distribute one another’s processing experiences rather than have the same experience. An e-learning environmental satisfaction level includes LMS, learning content, and the service quality of interaction (Lee et al., 2004). Therefore, the following hypothesis should be considered:

 Hypothesis-7 (H7): A learner's assessment of the service quality of interaction between a professor and learner will be positively related to the e-Learner’s satisfaction level.

 As is generally known, LMS is one of many information systems used by the learner, in which learning content is considered to be very important. Especially, the assessment levels regarding information quality are classified as intrinsic quality, contextual quality, representational quality, and accessible quality (Lee et al. 2002). In terms of e-learning content quality, information quality is defined as content quality in conjunction with contextual and representational quality. Therefore, the following hypothesis should be considered:

 Hypothesis-8 (H8): A learner's assessment regarding content quality will be positively related to the e-Learner’s satisfaction level.


 This research has been conducted within an empirical study by survey method. Students enrolled in e-learning courses at D University and H University responded to a poll conducted during the regular semester. The 358 participating students had taken cyber courses in 4 different subjects offered at the above Universities. The missing values of 17 questionnaires were rejected, and 341 copies of an analysis questionnaire were collected. All sample distributions are illustrated in Table 1. The analysis tool was designed and used in conjunction with SPSS software and PLS (partial least square) Graph 3.0 software. PLS requirements regarding sample size are not restrictive (Chin, 1998). For the reason, PLS Graph 3.0 software approaches are based on components (Chin, 1998).

Table 1. Convergent validity analysis

Analysis Results

 Construct reliability in the study was proven as shown in Table 1.

 Generally, the Cronbach's alpha value should be at least 0.7, in order to ensure the reliability of the survey (Nunnally, 1978). The Cronbach's alpha value in this research was higher than 0.78. Composite reliability is an index designated to measure the convergent validity. The standard value should be at least 0.8 (Nunnally, 1978). The composite reliability value in this research was higher than 0.842. Therefore, the measurement model in regards to this research model contains convergent validity. If the square root of average variance extract (AVE) is greater than the correlation coefficient between the different factors, the presence of discriminate validity is assumed (Fornell & Larcker, 1981). Each AVE value in regards to this research model has a discriminate validity of higher than 0.755. The suggested measurement model is estimated as having positive discriminate validity, due to the square root of the AVE, as illustrated in Table 2.

Table 2. Correlations of latent variables

 All hypotheses, from H1 to H8 have been accepted. Figure 2 illustrates the PLS analysis results regarding the structural model. Table 3 illustrates the results of each hypothesis analysis.

Figure 2. PLS analysis result of the structural model

Table 3. Inferred value of research model

Discussions and Implications

 Table 3 illustrates the results of the analysis on each hypothesis. In the full model, every R2 value is higher than 10% which was suggested by Falk and Miller (1992) as shown in figure 2. All hypotheses from H1 to H8 have been accepted. It is explained that system quality and e-learner’s characteristics are positively related to the real e-learning courses performances

 The suggested model in regards to e-learning systems supports the revised ISS model theory proposed by Delone & Mclean (2003). System quality, service quality, and information quality are also important variables in regards to e-learning systems.

 A learner’s self-regulatory learning strategy and self-regulatory efficacy are also important variables compared to satisfaction in regards to scholastic performance. According to Bandura (1986), self-regulatory efficacy is composed of efficacy or well-performed self-regulatory mechanisms, such as self-observation, self-judgment, and self-response. This research findings in LMS environment support Bandura’s self-regulatory efficacy opinions.

 In addition, an e-learner’s cognitive empathy in this research is a meaningful factor and emphasized with self-regulatory efficacy. The information society of the past has been transformed into today’s emotional society (Daniel Pink, 2005). The proposed six conditions regarding a competent person in regards to the future are comprised of design, story, symphony, empathy, play, and meaning grant (Daniel Pink, 2005). The ability to empathize for collaboration is a meaningful concept in a competent environment. As well, Halpern Jodi (2001), a bioethics scholar, reported that it is possible to accurately diagnose by complementary objective knowledge through empathy added to utilize scientific technology and other tools. In addition, empathetic power emerged as a necessary talent for sustaining new insights about medical activities. As well, it is important to trust in the communication value for a more effective feeling of alignment with a patient.

 This research model focused on one’s ability to empathize, and proved that cognitive empathy constituted a meaningful construction in the e-learning environment. In this paper, in similarity to the field of medical science, an e-learner’s cognitive empathy strengthens self-regulatory efficacy, and affects his or her expected performance outcome (Lee, 2008). As well, meta-cognition is comprised of a host of factors affecting self-directed learning. It is also used in the fields of human-computer interaction and computer engineering. In addition, the ability to empathize for collaboration is a meaningful concept in regards to the expectations and outcomes regarding learning, self-regulation, and medical treatment (Lee, 2008). This study’s results proved the validity of opinions referred to, and confirmed the power of cognitive empathy.


 In so far as a result of this paper, findings that were based on a large cross-sectional study involving e-learners and a LMS have been reported, focusing on the concepts of a learner’s characteristics. Also educational engineering concepts were used. With the proposed mode titled “an e-learning success model” the study attempted to assess an e-learner’s scholastic performance with e-learning based on LMS, by adopting a self-regulated learning strategy, self-regulatory efficacy, and the e-learner’s cognitive empathy. Conclusions are as follow:

 First, this study emphasizes the strategic improvement of self-regulated learning strategies in regards to e-learning. An e-learner’s scholastic performance is also influenced by self-regulated learning strategies with the LMS satisfaction.

 Second, an e-learner’s satisfaction level requires quality assessments regarding LMS. System quality and information quality as well as service quality through LMS are very meaningful components. A number of researchers have emphasized the importance of interaction or the quantitative side of interaction, but failed to consider how to utilize this information. That is, they failed to emphasize the assessment method considerably and necessarily in terms of interaction quality. The quality of the interaction has had a significant effect on learner satisfaction, as illustrated in this study.

 Third, a learner’s cognitive empathy is a meaningful variable regarding an e-learner’s scholastic performance, such as in regards to the field of medical science education as above Halpern Jodi report (2001). It is necessary to learn how to achieve competent student empathetic abilities. In so doing, this can lead to the learner’s potential increase in collaboration ability and upgrade his or her performance level.

 Fourth, an e-learner’s self-regulatory efficacy is also a meaningful learner characteristic in regards to his or her scholastic performance. First of all, self-regulatory efficacy increases learner’s expected performance and self-observation, and self-directed learning ability in LMS.

 Nevertheless, this paper was limited in scope on the basis of an e-learner’s scholastic performance. Further studies regarding differing large-scale assessments are required. As well, researches for performance differences between general learner and e-learner will be studied.




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