MOTIVATION AND LEARNER CHARACTERISTICS AFFECTING ONLINE LEARNING AND
LEARNING APPLICATION
DOO H. LIM HYUNJOONG KIM
Uni ver sity of Ten nes see, Knox ville
ABSTRACT
Many studies have been conducted to verify the effect of learner charac -teristics and motivation in traditional classroom, but very few are found in online learning research. This study sought to identify what learner character -istics and motivation types affected a group of undergraduate students’ learning and application of learning for a course conducted online. Utilizing quantitative and qualitative analyses, the study found that gender and employment status affected online learners’ learning and learning application. Several motivation variables were also found to significantly influence online learners’ learning application. Discussions of instructional strategies to promote learner motivation and satisfaction in online learning environment were included.
INTRODUCTION
Learning and teaching in a virtual learning environment has been an alternate method for several decades to overcome limitations of traditional classroom instruction and meet diverse learning and instructional needs from students and teachers. Even though many studies have attempted to answer research questions about online teaching and learning from various perspectives in instructional design and technology, very few have sought to address motivational issues in
423 2003, Baywood Pub lishing Co., Inc.
online teaching and learning. Some motivational variables were found to influence learning in general, but others are unique to online learning environment as they influence online learners’ performance and satisfaction [1]. Learner character -istics are another variable known to affect students’ learning achievement both for traditional and online instruction. Some example learner characteristics identified are gender [2], previous work experience [3], computer experience [4], and previous online learning experience [5]. As more learners are exposed to online learning environment in public and private organizations, the need to identify instructional conditions and strategies that promote online learning motivation and accommodate learner characteristics is growing more than ever.
One problematic situation in online learning as well as traditional classroom environment is that most instructors tend to focus on mastering the subject content of a course without providing opportunities to apply and transfer the learned content to individual learner’s jobs, tasks, and personal career [6, 7]. Imposing significant challenges on recent research studies focusing only on learning aspect of online instruction, identifying what instructional and learner variables facilitate application of learning in such learning environment is believed to be a meaningful study that researchers should seek for. To address this kind of learning application issue in online instruction, the findings of this kind of study are expected to reveal valid motivational and learner characteristics variables that influence online learners’ application of learning as well as learning achievement.
CONCEPTUAL FRAMEWORK OF ONLINE LEARNING MOTIVATION
In reviewing many research studies on learning motivation, several theories were considered valid to explain how individual learner is motivated to learning. First, social learning theorists propose that an individual’s belief about the con -tingency of reinforcement influences learner behavior [8, 9]. According to them, learners will attain a high degree of learning achievement when they can relate their ability or effort to instructional rewards such as grades and instructional feedback. Other researchers adopt a similar motivation construct with the social learning theorists. In Vroom’s expectancy theory, certain behaviors are followed by desirable outcomes or incentive awards [10]. Pajares and Miller suggest learner’s behavior will occur if there are various stimuli, known as outcome relations, or if she or he follows certain behaviors, known as outcome relations [11]. Attribution theorists such as Atkinson [12] and Weiner [13] perceive a more elaborate view on human motivation. While Rotter [8] regards ability and effort as intrinsic motivation sources that are both con -trollable, Weiner refers ability as a stable cause that is uncontrollable and effort as controllable from one situation to another. Atkinson’s expectancy-value theory 424 / LIM AND KIM
explains an individual’s achievement motivation as a function of “the motive to achieve success (Ms),” “the probability of success (Ps),” and “the incentive value of success (Is).” That is, individual learning motivation can be explained by the combined force of Ms × Ps × Is. Other motivation theorists such as Deci [14], Herzberg, Mausner, and Snyderman [15] also share a very similar motivation construct with the Expectancy Theory, expressed as “Motivation forces = Expectancy × Instrumentality × Valence.” Malone considers challenge, fantasy, curiosity, and control as the four intrinsic dimensions of learning motivation [16]. In an attempt to apply various motivation theories to instructional design, Keller [17] has developed a model of motivational instruction design known as the ARCS model [17]. Keller’s ARCS model comprises four sub-components for learning motivation. Attention is to arouse and sustain curiosity and interest. Relevance is to provide close links between course content and learners’ needs, interests, and motivation. Confidence is related to how learners expect to succeed in an instruction. Personal expectation for success, difficulty of tasks, locus of control, and personal causation contribute to learner confidence. Satisfaction is derived from achieving performance goals.
While the above mentioned motivation theories and constructs tried to explain how an individual is motivated to learn in traditional classroom settings, a common set of motivation variables that are unique to online instruction is needed to be identified. To establish a theoretical base to address the purpose of the study, the researchers developed a conceptual framework of online learning motivation based on various motivation theories and constructs. Reinforcement
is the first type of motivation used for the study. Grades, instructor feedback, peer support, and technical support are some examples of reinforcement moti -vation. Several theorists have noted reinforcement as an important learning motivation [9, 13, 18]. Relevance refers to the value of course content related to learner’s jobs and studies. Atkinson [12], Deci [14], Herzberg, Mausner, and Snyderman [15] indicate value residing in learning content decides the level of motivation. Interest is another type of motivation promoting learner involve -ment. When a learning task is challengeable and utilizes fantasy to present learning content, learners will be motivated [16]. Self-competence is influenced by feelings of self-worth and self-efficacy which is internal to learners. competence can be generally described as the degree to which one believes that he or she is able to achieve a given task [19]. Dweck suggests that competence can positively or negatively affect learner motivation [20]. Affect
influences a learner’s feeling and emotion in learning and transferring of learning. An individual’s affect is influenced by corporate culture and climate, opinions of co-workers and supervisors, family situations, present state of mental health, attitude toward incorporating newly acquired knowledge and skills into everyday tasks, attitude toward change, and degrees of frustration, joy, determination, and gratification in utilizing newly acquired knowledge and skills [21].
METHODOLOGY Pur pose
Even though there have been abundant research studies to verify the effect of learner characteristics and motivation on learning performance in traditional learning environment, very few have been conducted to assess the effect of those variables on learner’s application of learning during online instruction. Here, the term “application of learning” refers to the degree to which learners use and apply learned knowledge and skills during instruction or to current jobs. To investigate the purpose of this study, several research questions were developed.
1. What is the degree of learning and application of learning perceived by a group of online learners in taking an online course.
2. What kinds of learner characteristics affect the online learners’ learning and learning application?
3. What types of motivation affect the online learners’ learning and learning application?
Meth od ol ogy
This study utilized qualitative and quantitative research methods to explore the research questions. The subjects for this study included 77 undergraduate students (23 male and 54 female) who took an online course between 2000 and 2001 at a southeastern university. Regarding employment status, 12 students were full-time students, 33 students had part-time jobs, and 32 students had full-time jobs. A questionnaire was developed to obtain the students’ level of motivation and perceived degree of learning and learning application in taking an online course. The students were asked to participate in the pre and post survey conducted online at the beginning and the end of each semester. The online questionnaire used a 5-point Likert-type scale to measure the degree of the learning (1 for “do not understand” to 5 for “completely understand”), learning application (1 for “none” to 5 for “frequently use”), and motivation (1 for “strongly disagree” to 5 for “strongly agree”). The questionnaire included question items composed of 18 learning objectives of the online course, and the average score of them served for the perceived degree of learning and learning application. The level of motivation was measured by a motivation questionnaire composed of 20 question items. The average scores were also calculated and used for the subsequent analysis. Basic descriptive statistics was used to analyze the degree of learning, appli -cation of learning, and motivation perceived by all students. Paired t-test was used to compare population mean scores for the perceived learning increase before and after the course. Analysis of covariance was carried out to verify the dependence of motivation and the degree of learning and learning application. Qualitative analysis was conducted to categorize the reasons that promote or hinder learning and application of learning responded by all students.
Con text of the On line Course
The online course was developed to teach curriculum content in program evaluation for undergraduate students. The online course included 13 learning modules and the workload of one module was equivalent to that of one week’s classroom instruction. Four sub-learning sections comprised one learning module. All students were asked to attend a class meeting for course orientation and another one for group project presentation at the end of each semester. At the orientation meeting each student was allowed to choose a learning schedule option among the two: weekly fixed schedule and self-paced flexible schedule. After selecting a schedule option, students in each schedule option were divided into peer groups composed of three to five students. Each peer student group was involved in a group project and various online discussion activities for group engagement and learning.
RESULTS De gree of Learning and Learning Ap pli ca tion
The mean scores of students’ learning are summarized in Table 1. The differ -ences between the pre and post learning scores are averaged and the paired t-test was conducted. Furthermore, descriptive statistics and paired ttests are imple -mented for each category of the characteristics variables such as gender, work experience, marital status, distance education experience, and age.
Overall, a significant learning increase was observed when compared between the beginning and the end of the course (p < .0001). Furthermore, a significant learning increase has occurred for all categories except those who have no distance education experience (p = .0617). In order to measure the statistical significance of the influence of learner characteristics on learning, an Analysis of Covariance (ANCOVA) was performed. In this analysis, the post learning score was cal culated as response and the pre learning score as covariate. Gender, work experi -ence, marital status, distance education experi-ence, and age were simultaneously considered as factors in the ANCOVA model. Table 2 lists each factor’s estimated effect and their p-values. It turns out that only gender variable is statistically significant. That is, female students show significantly higher degree of learning than male students; but other variables in student characteristics do not have significant effect on learning difference.
From the qualitative analysis of the reasons for high leaning and application of learning during instruction, several findings explain what may cause the gender difference in the online learning (see Table 3). First, female students perceived the instructional quality of the course relatively better than the male students. Among several instructional factors, female students perceived some factors such as instructional effectiveness, previous learning experience, and opportunity to
Table 1. Learning Dif fer ences between the Pre and Post Survey Learning mean scores
Mean diff. (SD) PT-value-test No. Pre (SD) Post (SD)
Gender Male Female Work Experience Full-time Part-time Not employed Marital Status Married Single DE Experience No Yes Age 18-29 30 or higher Overall 23 54 12 33 32 53 24 7 70 67 10 77 3.09 (.15) 3.15 (.09) 3.02 (.10) 3.09 (.16) 3.54 (.36) 3.09 (.16) 3.14 (.12) 2.83 (.28) 3.16 (.08) 3.14 (.09) 3.11 (.16) 3.13 (.08) 3.73 (.15) 4.16 (.08) 3.97 (.11) 4.07 (.13) 4.44 (.13) 3.96 (.12) 4.14 (.10) 3.83 (.31) 4.05 (.08) 4.06 (.09) 3.96 (.14) 4.03 (.08) 0.64 (.13) 1.01 (.12) 0.94 (.15) 0.98 (.16) 0.90 (.28) 0.87 (.14) 1.00 (.13) 1.00 (.43) 0.89 (.09) 0.92 (.11) 0.84 (.15) 0.90 (.09) < .0001 < .0001 <.0001 < .0001 0.012 < .0001 < .0001 .0617 < .0001 < .0001 < .0001 < .0001
Table 3. Rea sons for High Learning and Appli ca tion by Gender Reason category Male (%) Female (%) All (%) Reasons for high learning
Instructional effectiveness
Related to my current or future jobs Previous learning
Opportunity to practice learning High interests in the learning content Personal motivation for learning Personal learning effectiveness Other reasons
Reasons for high application Opportunity to use learning Because of high learning
Applicable learning content to my work Personal motivation to apply
Personal interest
Because of repetition of information
16 (36) 5 (12) 3 (7) 2 (4) 6 (14) 7 (16) 3 (7) 2 (4) 44 13 (48) 5 (18) 3 (12) 5 (18) 1 (4) 0 (0) 27 49 (53) 9 (10) 11 (12) 11 (12) 6 (6) 3 (3) 4 (4) 0 (0) 93 48 (68) 7 (10) 9 (13) 0 (0) 4 (6) 2 (3) 70 65 (47) 14 (10) 14 (10) 13 (10) 12 (9) 10 (7) 7 (5) 2 (2) 137 61 (62) 12 (12) 12 (12) 5 (5) 5 (5) 2 (2) 97 Table 2. Esti mated Effect of Stu dent Char ac ter is tics
on Learning Increase
Variable Description of effect Esti mated effect(Stan dard error) ANCOVAP-value Gender
Work Experience Marital Status DE Experience Age
Pre Learning Score
Female–Male
Full time–Not employed Part time–Not employed Single–Married No–Yes “18-29”–“30 or higher” 0.42 (.16) –0.41 (.16) –0.38 (.22) 0.27 (.20) 0.04 (.34) 0.25 (.21) 0.24 (.10) 0.011 0.201 0.182 0.918 0.239 < .0001
practice learning as positively promoting factors for their learning. Opportunity to use learning was responded relatively frequently by the female students as it positively promoted their learning application. Unlike the degree of learning, learning application was surveyed only one time at the end of the course. Therefore, the researchers used the one-way analysis of variance (ANOVA) to test the significance of each variable on learning application. The mean learning application scores, their standard errors, and the p-values from the one-way ANOVA are listed in Table 4. In this analysis it was identified that the female students had significantly higher application score than male students and time employed students showed significantly lower learning application score than non-employed students. Because the variables can be confounded each other, the researchers also fitted the multi-way ANOVA by putting all variables simultaneously in the model. It turns out that the same conclusion still holds.
Learner Mo ti va tion Ef fect on Learning
Five different types of motivation—course relevancy (CR), course interest (CI), affect/emotion (AE), reinforcement (RI), self-efficacy (SE)—were included in this study. Among the five motivation variables, CR was indicated as the most important factor followed by SE and RI. To identify how each motivation variable affected students’ learning and application of learning, the motivation scores were tabulated by the low and high motivation group where the two groups were divided by the median of the motivation scores (see Table 5).
In this analysis, the ANCOVA model was used in order to measure if the different level of each motivation variable affected students learning. Since gender was a significant variable that affected the learning score, it was also included in the ANCOVA model. Two-step processes were utilized in the analysis. First, the ANCOVA model was fitted individually for each motivation variable. That is, the researchers considered one type of motivation at a time as a covariate; the post learning score was treated as response, the gender as factor, and the pre learning score as another covariate. Second, the researchers included all types of moti -vation simultaneously as covariates in the model. The results are included in Table 6. It was found that all motivation variables except CI showed a significant effect on student learning in the individual ANCOVA model. However, in the simultaneous ANCOVA model, only RI and SE were identified as the significant motivation variables influencing students’ learning. This inconsistent result implies there may exist some confounding effects among the motivation variables and they may be correlated and difficult to separate one from another.
To verify what caused such inconsistent result, the correlation analysis and Factor analysis were carried out. The correlation analysis in Table 7 indicates that there are significant linear relationships among motivation variables; especially among CR, AE, RI, and SE. It is also noticeable that CI does not have strong 430 / LIM AND KIM
relationships with others. In particular, CI and RI have very low correlation. The factor analysis also confirmed that CI, AE, RI, and SE belong to the same factor group and CI is in different factor group. Using the averaged motivation score and CI as covariates, the researchers performed a similar ANCOVA analysis as in Table 8.
In summary, as the average motivation score increases the perceived degree of learning also increases significantly. However, the CI does not show a significant effect on learning.
Ef fect on Learning Ap pli ca tion
As shown in Table 5, it appears that high motivation group has higher application scores across all types of motivation. To confirm the significance of the effect of each motivation variable on learning application, an ANCOVA
Table 4. Effect of Stu dent Char ac ter is tics on Learning Application
Variable (Stan dard error)Mean score
One-way ANOVA P-value Multi-way ANCOVA P-value Gender Male Female Work Experience Full-time Part-time Not employed Marital Status Married Single DE Experience No Yes Age 20-29 30-Overall 3.50 (.14) 3.95 (.09) 3.65 (.22) 3.87 (.12) 4.31 (.13) 3.64 (.14) 3.95 (.11) 3.75 (.27) 3.82 (.09) 3.91 (.09) 3.59 (.15) 3.82 (.08) .0109 .0336 .0792 .8016 .0735 .0131 .0423 .3552 .9511 .8487
analysis was performed again. From the previous analysis result found in Table 4, the researchers acknowledged that gender and work experience had a significant effect on learning application. To properly detect the effect of motivation, gender and work experience were also included in the ANCOVA model as factors. Two ANCOVA models were utilized; individual modeling for each motivation variable and simultaneous modeling using all motivation variables. Table 9 shows the results of the two ANCOVA analyses. Both gender and work experience are significant factors across all models. Female students have higher learning appli -cation scores than males; non-employed students have higher scores than either full-time or part-time employed students. Among the five motivation variables, RI and SE are significant for the individual and simultaneous ANCOVA models. It is also noted that the results of the individual and simultaneous ANCOVA are consistent.
DISCUSSIONS Learner Char ac ter is tics
As the findings indicate, the online course seemed to meet the course goal to achieve significant learning increase before and after the course. In terms of the learner characteristics, this study supports the argument that female students learn better than male students in an online learning environment [2]. One unique finding from this study, however, is the fact that the gender effect also exists on the aspect of learning application. Moreover, the qualitative findings revealed that 432 / LIM AND KIM
Table 5. Moti va tion and Learning Appli ca tion Scores by High/Low Moti va tion Group
All Moti va tion means (Low-High groupSD) Appli ca tion means (SD) Motivation type Mean (SD)
Low group High group Low group High group Course relevancy Course interest Affect/Emotion Reinforcement Self-efficacy 4.69 (.43) 4.29 (.49) 3.95 (.72) 4.52 (.71) 4.57 (.48) 4.30 (.38) 3.91 (.31) 3.31 (.49) 4.06 (.37) 4.19 (.39) 5.00 (.00) 4.73 (.23) 4.50 (.33) 4.92 (.14) 4.95 (.10) 3.66 (.14) 3.83 (.13) 3.79 (.14) 3.57 (.13) 3.53 (.15) 4.01 (.13) 3.88 (.14) 3.91 (.13) 4.10 (.12) 4.05 (.11)
Motivation P-value, Coef fi cient (stan dard error)Indi vid ual ANCOVA Simul ta neousANCOVA Course relevancy Course interest Affect/Emotion Reinforcement Self-efficacy Gender Pre-score P = .0103, 0.45 (.17) P = .0094, 0.42 (.16) P = .0094, 0.25 (.09) P = .5566, 0.09 (.16) P = .0203, 0.40 (.17) P = .0104, 0.26 (.10) P = .0091, 0.28 (.10) P = .0066, 0.45 (.16) P = .0097, 0.25 (.09) P = .0003, 0.53 (.14) P = .0147, 0.37 (.15) P = .0310, 0.20 (.09) P = .0002, 0.61 (.15) P = .0019, 0.48 (.15) P = .1172, 0.14 (.09) P = .5693, 0.10 (.18) P = .5807, –0.08, (.14) P = .4598, 0.08 (.11) P = .0503, 0.31 (.15) P = .0251, 0.40 (.17) P = .0027, 0.45 (.14) P = .1200, 0.14 (.09)
female and male students had utilized different learning strategies throughout the course. Compared to male students, female students considered some instructional factors and learning activities more valuable for their learning than other factors. Those include clear and concise learning content, usefulness of class assignment and projects, review and repetition of previous learning, and opportunity to use learning. Male students considered high interest and personal motivation to learning as more meaningful reasons for their high learning. From this finding, it can be naturally interpreted that female students seemed to attribute their successful learning experiences from external sources such as instructional design factors while male students find those from internal orientation such as personal interest and motivation.
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Table 7. Cor re la tion among the Dif fer ent Types of Motivation Course relevance Course interest Affect/ Emotion Reinforcement Self-efficacy Course relevance Course interest Affect/Emotion Reinforcement Self-efficacy 1 0.24 0.46** 0.36** 0.43** 0.24 1 0.33* 0.08 0.23 0.46** 0.33* 1 0.41** 0.38** 0.36** 0.08 0.41** 1 0.44** 0.43** 0.23 0.38** 0.44** 1 *p-value < .05. **p-value < .01.
Table 8. ANCOVA Anal y sis Using Aver age Score of the Fac tor Group
Independent variables Esti mated effect(stan dard error) ANCOVAP-value Average of CR, AE, RI, and SE
Course interest Gender Pre-score 0.82 (.17) –0.11 (.14) 0.46 (.14) 0.18 (.08) < .0001 .4479 .0021 .0366
Motivation P-value, Coef fi cient (stan dard error)Indi vid ual ANCOVA Simul ta neousANCOVA Course relevancy Course interest Affect/Emotion Reinforcement Self-efficacy Gender Work experience Full-time Part time P = .0672, 0.37 (.20) P = .0047, 0.54 (.16) P = .0194, –0.71 (.18) –0.49 (.25) P = .2751, 0.20 (.18) P = .0055, 0.55 (.19) P = .0115, –0.80 (.19) –0.56 (.26) P = .3989, 0.10 (.12) P = .0064, 0.54 (.19) P = .0226, –0.72 (.19) –0.50 (.25) P = .0004, 0.60 (.16) P = .0054, 0.48 (.17) P = .0339, –0.59 (.17) –0.52 (.22) P = .0006, 0.61 (.17) P = .0010, 0.59 (.17) P = .0122, –0.70 (.17) –0.49 (.23) P = .7345, 0.07 (.21) P = .4651, 0.12, (.17) P = .1525, –0.19 (.13) P = .0085, 0.49 (.18) P = .0305, 0.42 (.19) P = .0024, 0.53 (.16) P = .0189, –0.65 (.17) –0.55 (.22)
Regarding learning application, job employment status was found to signifi -cantly affect students’ application of learning along with the gender effect. It would be logically assumed that students with employment status would experi -ence more learning application opportunities than those who without jobs, but the finding revealed a reverse result. Several reasons may account for this result. First, the questionnaire asked the perceived degree of learning application during their learning period. Considering each individual student’s different learning condition, one possible inference might be that fulltime students would have had a greater intention to apply their learning to the class learning activities, assignments, and course projects. Second, as possibly explained, full-time students would have committed more to their learning in terms of the time spent on studying learning modules and interactions with the instructor and other students than those students with jobs who had less time to apply learning due to work constraints during their study period.
On line Learning Mo ti va tion
The major finding of this study is that all motivation variables except course interest seemed to affect students’ learning while reinforcement and self-efficacy influenced students’ learning application. It is worth devoting some discussions as to why course interest did not make a significant influence on students’ learning and learning application. From the qualitative findings, students perceived personal interest as a relatively less important reason to their high learning and application compared to other reason categories. Among all responded reasons for high learning and application, only 12 (9 percent) and 5 (5 percent) responses were accounted for course interest as it helped students’ learning and learning application respectively. Course interest also did not show strong relationship with other motivation variables (except affect/emotion) and the learning and application scores as well.
In struc tional Strat egies to Aug ment Learning
The focus of this research study was to identify if learner characteristics and different types of motivation influence students’ learning and their application of learned content during their studies. As discussed, gender and job employment status were found to be such learner characteristics affecting students’ learning and application. Motivation variables, as a whole (except course interest), seemed to signifcantly influence students’ learning. The remaining question is then how to design online instructions that fully accommodate those differences in learner characteristics and promote learner motivation to result in better learning outcomes and transfer. Stemming from various learning theories, several instructional strategies are recommended as viable options. First, making online instruction in such ways that provide ample opportunities to practice learning is critical for meaningful learning and transfer. Reflective learning theorists refer this 436 / LIM AND KIM
method as action oriented learning that stretches learning experiences into new appreciations of novel situation and working knowledge [22]. Customizing assignments and class projects to incorporate students’ own job examples and experiences would be another strategy to expand the application opportunities for those students with jobs. Situated learning theory presses the critical role of real-world practice during learning by engaging learners in authentic, solving situations [23]. In problem-based learning, realistic problems are believed to provide motivation to apply learning to the real situation [24]. To best promote active and engaged learning for students, several motivational strategies deem to be effective. Among the studied motivation variables, course relevancy, reinforce -ment, course interest, and affect/emotion are controllable, but self-efficacy is not. To facilitate these types of controllable learning motivation, applying collab -orative learning principles to designing interactive learning activities is considered a very valid strategy. This is because the collaborative learning activities empha -size the co-construction process of student learning and idea sharing to solve real life problem [25]. Through the diverse learning interactions such as observation, interpretation, construction, contextualization, multiple manifestation, ownership of knowledge, and self-awareness of learning process, students will attain a high degree of learning motivation and involvement [26, 27]. Timely feedback and support are another considerations for learner motivation. The weakest aspect of online instruction has been said to be the lack of instructor student relationship through “eye to eye” and “tongue to tongue” communication that creates online learners’ emotional involvement in the learning process. Checking students’ learning progresses and sending frequent e-mails for feedback and encouragement will be an easy but effective strategy to increase students’ emotional involvement with the instructor and in the learning process.
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University of Tennessee
408 Stokely Management Center Knoxville, TN 37996-0545 e-mail: [email protected]
