Impact of Learning Strategies and Motivation on Performance: A Study in Web-Based Instruction
Siva R. Sankaran
This study investigates how learning strategies and motivation influence performance in Web and lecture settings of a business information systems course. These were measured using a survey instrument: learning performance by test scores. Findings suggest that using either deep or surface learning strategy leads to comparable positive performances, but undirected strategy affects performance negatively. While motivation is significantly correlated to performance in both Web and lecture, the relationship is stronger in the Web setting. High motivation is associated with the use of deep learning strategy, and low motivation with undirected strategy. Pre-post test analysis shows that learning strategies and motivation are also correlated with gains in incremental scores. The results have implications for course design and instruction by taking individual differences into account.
Distance education is the process of instruction and learning via virtual classrooms where teachers and students are separated in space and sometimes in time. Today, distance education plays an important role in the rapidly changing society that places continual demand on learners. While television and video-teleconferencing were prevalent during the seventies and eighties, the Internet is taking the center stage today as the preferred medium of delivery for distance education due to its versatility and low cost (Moskal, 1997 & Sopova, 1996). A growing number of universities are embracing it than ever before.
Objective
The purpose of this research was to study how learning strategies-deep, surface, undirected- and motivation affect learning performance in Web-based instruction as compared to a traditional lecture setting (Figure 1), The goal was not merely to compare learning effectiveness of Web vs. lecture setting, but part of an overall investigation of why individual student performances vary even though the same course content is delivered to all of them (Sankaran & Bui, 1999 & 2000).
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Research in Distance Education
During the evolution of the various delivery technologies of the last three decades, researchers have explored several issues in distance education. The major ones among them are: effectiveness, student background, learning style, motivation, course design, instructor role and cost-benefits (Ragothaman & Hoadley, 1997 & VanZile-Tamsen & Livingston, 1999). Of these issues, this study focuses on (i) effectiveness. (ii) learning strategies and (iii) motivation.
Effectiveness
A principal question that has interested researchers In the past has been whether distance education is as effective as traditional lectures. Many studies evaluated effectiveness in terms of test scores and grades in a distant learning setting and compared them with those in the conventional classroom. Valore and Diehl (1987) examined research published since 1920 on correspondence studies, and concluded that correspondent students perform just as well as their classroom counterparts. Kuramoto (1984) evaluated – face to face, teleconferencing, and correspondence study – and concluded that all three were equally effective. Souder (1993) compared performances of two groups of graduate students, one taught in traditional lecture format and the other using satellite broadcasting, Results showed that distance learners performed better than their classroom counterparts. Based on an extensive review of literature, Porter (1997) contends that distance education is at least as effective as that of traditional lectures. Learning Strategies
One of the problems with many earlier effectiveness studies is that only the net performance of a group of distance education students has been measured. However, one can see that the individual students may react differently to distance learning due to differences in their background. Two such background variables are Learning Strategies and Motivation.
Learning strategies refer to the activities by which learning is achieved. For example, reading aloud, copying notes, consulting peers, asking the instructor for clarification are all learning strategies. The use of learning strategies allows students to actively process information, thereby influencing their mastery of material and subsequent academic achievement (Pintrich, Smith, Garcia & McKeachie, 1993).
Hoekscma (1995) proposed two types of learning strategies: deep and surface. A deep learning strategy is directed at understanding the meaning of a task and to satisfy curiosity. A student using the deep will put in longer study hours, make detailed notes from the text and class Web site, do exercises in addition to meeting the minimum assignments, and will study continually rather than cram (Vermunt, 1998). It may be considered the highest form of learning, A surface learning strategy, on the other hand, is directed to memorizing facts, disjointed pieces of data. examples and illustrations (Hoeksema, 1995). A student using the surface strategy will have a reproducing orientation trying to memorize pieces of information and more interested in getting good grades without having to fully master the material. In practice, many students using the surface strategy have been found to be successful because deep level learning are just not required to satisfy many examination requirements (Vermunt, 1998 & Busato, 1998).
Vermunt (1992) reported on a learning behavior he referred to as an undirected learning strategy. Students using this strategy have problems in processing and coping with the amount of material to study. They also have problems with discriminating what is `important and what is not. The undirected learning strategy is similar to the non-academic orientation described by Entwistele and Ramsden (1983). Busato, et. al (1998) and Vermunt (1996) found that the undirected learning was a negative predictor of academic success. Motivation
Educators in general believe that all students can learn. However, the strength of desire and temperament to learn varies from one student to another. Some learn for the sheer purpose of knowledge and the intrinsic satisfaction it brings. Others are motivated by the external rewards such as getting an “A” grade or getting a job. In the real world, students bring a variety of cognitive and psychological readiness levels to the classroom. To be a successful learner, Schuemer [ 181 points out that the student must have a high degree of motivation.
Students who choose distance education need a high level of motivation if they are to complete the course work successfully. During their studies, they often have to work by themselves with little or no opportunities for face to face or peer interaction. They will have to deal with more abstract and ambiguous situations than someone taking a lecture class. They need to be efficient in time management, be responsible and in control of their studies and maintain an image of self-worth and self-efficacy. They should see the value of the education and be able to postpone current enjoyments and cope with interruption life frequently entails.
In general, knowledge of student learning strategies and motivational levels can be helpful to the instructor. The course may be designed to better fit with the students’ learning methods and motivational traits. Further, administrators can implement appropriate programs to improve student learning skills particularly for students with low motivation.
In summary, Web-based instruction is a relatively recent phenomenon and research in the area is in its infancy. More research is needed to build a theoretical foundation for Web-based instruction [ 10]. Many studies may have to be replicated and next ones undertaken to determine if the earlier findings in correspondence and telecourses are still applicable in Web instructional environment.
Hypotheses
The following hypotheses were tested.
HI Students using deep learning strategy will perform better than students using the surface learning strategy in Web and lecture settings.
H2 Students using surface learning strategy will perform better than students using undirected learning strategy in Web and lecture settings.
H3 There will be no difference in performance among students who use the same learning strategy whether they are in the Web or lecture group.
H4 The higher the motivation, the better the performance in both Web and lecture settings.
H5 There will be no difference in performance among students who have similar motivational levels whether they are in the Web or lecture group.
Method
The subjects for this study were students enrolled in an accelerated 4-week undergraduate business computer course. The course was offered in two alternative formats, lecture and Web. The instructor covered the same course content in both formats. The students were given a pre-test to measure their baseline knowledge of course content. At the end of the course, all students were administered the same test in a lecture hall. The test scores measured performance. The maximum score on the test was 75 points.
Learning Strategy and Motivation Survey,
A survey instrument was developed to quantify the learning strategies and the motivation level of each student. The instrument was developed over three iterations each time retaining only items that met the content validity requirement. Each item was a cafeteria style statement that described a learning strategy or a motivation aspect that the Student Could find her/himself in agreement or disagreement. An interval scale of I to 5 was used with I representing strong disagreement and 5 representing strong agreement, with 3 being neutral.
The Learning Strategy subscale contained 14 items to determine whether the student employed deep, surface, or undirected method in learning. The *internal consistency was tested by computing Cronbach alpha coefficient which came out to be 0.67. According to Nunnally [19], a value of over 0.5 is acceptable in sociological measurements. Some sample statements used for measuring the learning strategy were: I made my own detailed notes from the textbook and Web materials while preparing for this test”, I practiced many more exercises in the book in addition to the assigned homework.”, I am more likely to cram for exams at the last minute”. To detect possible agreement bias, some statements were reverse scored. The mean score of all the items was computed for each student and assigned as a Learning Strategies Score (LSS). The statements and the rating scale were designed in a manner that a higher LSS represented a student with a deep learning strategy and a lower score represented a student with an undirected learning strategy. The survey response range of I to 5 was divided into three equal parts, less than 2.3, 2.3 to 3.7, and greater than 3.7. Students with LSS of greater than 3.7 were considered in the deep learning strategy group: between 2.3 and 3.7 in the surface group, and those below 2.3 in the undirected learning strategy group.
Motivation was also measured using 14 items. The Cronbach alpha coefficient was 0.72. Some sample statements used for this subscale were: I can postpone current enjoyment (eg. attending a party) so that I can study for my test”, I am a good time manager and always find the necessary time to study”, “I feel I am the person responsible for how well I do in this class”. As in the case for learning strategies, the mean score of all the subscale `items was computed for each student and assigned a Motivation Score (MS). Students with a MS of greater than 3.7 were considered in the high motivation group-, between 23 and 3.7 in the moderate motivation group, and NIS below 2.3 in the low motivation group. The statements and the rating scale were designed in a manner that a higher score represented a student with a higher motivation. Hypothesis testing
For H I, the mean LSS were computed for the deep learning as well as surface learning group in the Web and lecture settings. The t-test was used to verify, if the scores arose from independent samples. A similar procedure was used to test H2, this time using the LSS for the surface and undirected learning groups. In order to test H3, first the mean LSS Of Students using the deep learning method in the Web group was compared using the t-test with that of the lecture group. To verify the rest of H3, the above step was repeated for the students in the surface and undirected learning groups. Since NIS and performance scores were continuous variables, H4 was tested using correlation analysis. Further, H4 being directional, a one-tail test was used. In order to test H5, as mentioned earlier, student performances were first classified into three groups according to their NIS – low, moderate and high. Student performances in similar motivation groups in Web and lecture settings were then compared using the t-test.
Results
There were 116 students in the sample, of which 60 were women and 56 men. There were 7 African Americans, 25 Asians. 3 9 Whites, 3 )5 Hispanics and 10 Middle Easterners. Forty-six chose to take the course in the Web format and 70 chose the lecture format.
HI: Influence of deep and surface learning on performance
In the Web group, students who used the deep learning strategy had a mean performance of 45.5 and students who used the Surface learning strategy had 46. 1. In the lecture group, students who used the deep learning strategy had a mean performance of 46.1 whereas students who used the surface learning strategy scored 44.6. Both differences were not significant (Table2).
Table 2
t-test for Equality of Mean Performance Scores
between Different Learning Strategies in Web and
Lecture Settings
t df P
Web Group
Deep-Surface 0.274 37 .785
Surface-Undirected 5.800(*) 29 .000
Lecture Group
Deep-SUrface 0.704 64 .484
Surface-Undirected 2.183(*) 42 .035
(*) Significant at 0.05 level
Thus, H 1, which stated that students using deep learning strategy will perform better than students using the surface learning irrespective of Web or lecture setting, was rejected.
It can be seen that students who used either deep or surface learning strategies performed equally well. The researcher had expected that students using deep learning strategy would perform significantly better than those using surface learning methods, but this was not the case. The reason can be that *in practice, deep learning Is not often required to satisfy, many university examinations [15]. Due to time and financial constraints, many students use surface learning when they are likely to be just as effective in completing the course 117
H2: Influence of surface and undirected learning strategy on performance
In the Web group, students who used the surface learning strategy had a mean performance of 46.1 and students who used the undirected learning strategy had 29.6 (Table 1). In the lecture group, students who used surface learning had a mean performance of 44.6 whereas students who used undirected learning strategy scored 34.5. Both differences were significant (Table 2). Hence, the hypothesis H2, that students using surface learning strategy will perform better than students using undirected learning strategy in Web and lecture settings, was supported.
Table 1
Comparison of Performance(*) by Learning Strategy
in Web and Lecture Settings
N Mean
Web Group
Deep 15 45.5
Surface 24 46.1
Undirected 7 29,6
Lecture Group
Deep 26 46,1
Surface 40 44.6
Undirected 4 34.5
(*) Measured by test score-, maximum score=75
Overall, the findings in this study show that employing either deep or surface learning strategy did not lead to a significant difference in performance-, but undirected strategy affected performance negatively.
H3: Influence of similar learning strategies in Web and lecture
H3 proposed that students who used similar learning strategies would perform comparably irrespective of Web or lecture format. Table 3 summarizes the findings. There was no significant difference in performance among students who used deep learning strategy in either format. Among those who used the surface learning strategy in Web or lecture, there was no difference in performance either. No differences were found in the undirected learning strategy group as well. Hence H3 was supported.
Table 3
Comparison of Test Scores by Learning Strategy
Levels in Web and Lecture Settings
N Mean t df P
Deep Learning .583 38 .563
Web Group 14 44.6
Lecture Group 26 46.1
Surface Learning .789 65 .433
Web Group 26 46.0
Lecture Group 41 44.4
Undirected Learning 1.114 9 .294
Web Group 7 29.6
Lecture Group 4 34.5
It is interesting to note that those who used deep learning method did perform slightly better in the lecture group, whereas those who used the surface learning did slightly better in the Web group. The explanation could be that the Web. with no direct verbal interaction, lends itself better to present course materials in a more structured manner. Important points in the material can be easier picked from the concise and organized Web materials than the detailed explanations in a lecture. Thus, the Web format is very conducive to one using surface strategy that emphasizes memorization and reproducing ability.
H4: Influence of motivation on performance
The results of the correlation analysis between motivation and performance scores for the Web and lecture groups are shown in Table 4. Both correlation coefficients were significant at 0.01 level. Therefore, the hypothesis that the higher the motivation, the better the performance in both Web and lecture settings was supported. The higher value of correlation for the Web group is worth noting. In distance learning setting, students undergo many sacrifices to get an education and motivation is a driving factor that influences their performance.
Table 4
Correlations between NIS and Performance
(Pearson r)
Performance
NIS (Web Group) .575(**)
NIS (Lecture Group) .317(**)
(**) p .01 (1-tailed)
H5: Influence of similar motivation levels in Web and lecture
H5 proposed that students who had similar motivation levels would perform comparably irrespective of format. It can be seen from Table 5 that student performances in each of the low, moderate or high motivation group did not in fact differ significantly in their scores between the lecture and Web format. Thus. H5 was supported.
Table 5
Comparison of test scores by motivation levels
N Mean t df p
Low Motivation 1.416 4 .230
Web Group 5 26.6
Lecture Group 1 22.0
Moderate Motivation .520 61 .605
Web Group 23 44.6
Lecture Group 40 43.6
High Motivation .119 45 .906
Web Group 18 46.4
Lecture Group 29 46.7
Additional Analysis
The amount of learning achieved by students in the course was computed by subtracting the pretest score from the final score. A correlation analysis was performed between the incremental scores, LSS and NIS (Table 6). The correlation coefficients confirm what one would hope, namely higher the learning strategy and motivation scores, higher the incremental learning attained during the course.
Table 6
Correlations between LSS, MS and Pre/Post-test
Incremental Score
LSS NIS Incremental Score
LSS 1.000 .834(**) .2’4 6(*)
MS .934(**) 1.000 .273(**)
(*) p .05 (1-tailed)
(**) p .01 (1-tailed)
The high correlation between NIS and LSS corroborates that those who have high motivation tend to use deep learning strategy. This empirically supports what was professed by earlier researchers [17, 20].
Analysis of variance of LSS according to the five ethnic groups showed that there were no statistical differences (F= 1.5311 `- p= 0.198). There NA ere no differences in NIS either (F=1.368; p=0.250). It can be seen from Table 7 that Hispanics had the highest learning strategy and motivation scores. They gained the highest incremental score between the pre-test and post-test. Even though Whites had lower LSS and MS, and ranked third in incremental score after Hispanics and Asians, they had the highest mean test score of any ethnic group. Analysis of data showed a possible causative factor for this was Whites had started off the Course with the advantage of the higher pre-test score.
Table 7
Mean LSS, MS. Performance and Pre/Post-test Incremental
Performance by Ethnicity
Post-test Pre/Post-test
N LSS MS Score Increment
African American 7 3.41 3.30 37.1 3.4
Asian 25 31.46 3.56 42.6 16.9
Hispanic 35 31.55 3.65 43.1 17.2
Middle Eastern 10 -11.11 3.48 41.9 8.6
White 39 3.10 3.26 47.7 11.8
Implications
Several lessons were learned from this study. First, a Web-course design that is well suited for students who use deep or surface strategy may not work for those who use undirected strategy. Hence, to make sure students with undirected strategy are not discouraged and lost, course design should include detailed learning objectives, learning reinforcers, step-by-step `instructions for assignments, review materials and sample questions.
Second, in our study, students with similar learning strategy and motivation performed equally well irrespective of Web or lecture format. Since the Course content in both formats in our study was the same, it appears instructors who are planning to offer their lecture courses in Web format may be able to adapt their material with minimum redesign. This is encouraging to instructors who are contemplating the use of distance education for the first time.
Finally, this study showed that students who are motivated the most also gained the most incremental learning. This underlines the importance of motivating students. Instructors and counselors should take time to demonstrate the value of a course and encourage them to postpone current enjoyment to the long-term benefits of a good education. Time management, self-efficacy, self-expectation and other strategic techniques may also be offered to students, especially to those who use undirected learning styles.
Conclusion
In a setting where the traditional face to face interaction is hard to institute, both educators and learners have many challenges to overcome in making distance education a rewarding experience. It is paramount that researchers in the field strive to identify, all the variables relating to student, instructor, design and cost that affect learning outcome. This study identified learning strategies and motivation as two possible student variables and explained their impact on learning performance. The long-tenn objective should be to undertake a program of research towards developing a theoretical framework for both Web learning and teaching. To be able to reap the full benefits of distance education, it is important for educators to match technology with the background and needs of the learners if education is to be effective.
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Siva R. Sankaran, California State University. Tung Bui, University of Hawaii.
Correspondence concerning this article should be addressed to Siva R. Sankaran, California State University, 18111 Nordhoff Street (COBAE), Northridge, CA 91330-8372.
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