Motivation and learning strategies of students in distance education.

A quantitative and qualitative study on the motivation and learning strategies of students in distance education was conducted during the spring 2001 and summer 2002 semesters within the course BSC 305, Evolution. The quantitative study was conducted using the Motivated Strategies for Learning Questionnaire (MSLQ) for measuring motivational and learning strategies. Data on motivation were collected at the beginning of the semester and data on the learning strategies were collected at the end of the semester. Scores from each subheading were correlated with the final grades. The following subscales may predict achievement in online courses: elaboration, organization, critical thinking, and effort management. Curiously, organization was found to be a negative predictor. For the qualitative study three sets of email questions were distributed at the beginning, in the middle, and at the end of the semester. Additionally, a telephone interview was conducted at the end of the semester. Students involved in the qualitative section were asked to take the MSLQ at the beginning and end of the semester along with the remainder of the class. Qualitative findings indicate that students need to organize their data. Findings also suggest that students require interaction from other students and people outside the class to feel successful.

Key words: learning strategies, motivation, distance education, online learning

INTRODUCTION

"Distance education is the process of instruction and learning via virtual classrooms where teachers and students are separated in space and sometimes in time" (Sankaran & Bui, 2001, p. 191). Sankaran and Bui claim that more universities are incorporating distance learning into their curriculum. In our rapidly changing world, distance education is playing an evermore important role. According to Miltiadou and Yu (2000), distance education is a dynamic and enriching form of learning. It offers an educational opportunity to those that may not be able to attend a traditional university. This type of instruction depends on "the Internet and computer-mediated communication systems for the delivery of instruction and interaction between students and instructors" (p. 4).

According to Vermunt (1996) "instruction does not lead to learning automatically" (p. 25). The outcome of students' achievement in the course depends on the learning strategies they use. Vermunt defines learning activities as, "thinking activities that people employ to learn, learning strategies as particular, often used combinations of learning activities' (p. 25). Olgren, (1998) points out that if we want students to learn, then we should have knowledge about how they learn. Improving the quality of learning should begin with this question, "What and how are people learning and what affects that process" (Morrison, 1989, as cited in Olgren, 1998, p. 77).

Filcher and Miller (2000) explain that learning strategies are vital to lifelong learning. Our society is going through a technological revolution and the workforce must adapt to the new technology and information in order to remain productive. People who have learned how to learn will continue to learn throughout their lifetime. According to Olgren (1998), "Distance education provides an avenue by which individuals can access this new information and continue to learn for the rest of their lives" (p. 61). Additionally, Olgren states that capable learners have the ability to design and carry out their own learning strategies.

Theoretical Basis. This research is based on a social-cognitive view of motivation and learning strategies. The student is an active processor whose beliefs and cognitions control instructional input and task characteristics. By emphasizing that students are in control of their learning, the Motivated Strategies for Learning Questionnaire (MSLQ) addresses advances in self-regulated learning (Garcia & Pintrich, 1995). McKeachie, Pintrich, Smith, and Sharma (1990) describe self-regulated learning as "a combination of cognitive and metacognitve involvement with a task, as well as motivational involvement with a task" (p. 4). Students are described as self-regulated learners to the level they are actively involved in their own learning process (Zimmerman, 1986). Zimmerman (1989) explains that these students will initiate and direct their own learning rather than having teachers do this. These students know when they are accomplishing their goals and when they are not. If they are not mastering the task, they find a way to succeed.

Pintrich (1995) explains that there are three components of self-regulated learning. The first component is the ability to control one's behavior, motivation and affect, and cognition. When students monitor their own behavior, cognition and motivation, they can then regulate these in order to achieve their goal. The second component is establishing a goal. Students must set a goal to accomplish which is the standard by which students monitor their progress. The third component is the individual student. If students change their behavior based on what someone else has suggested, this is not considered self-regulated learning. If a student is to monitor and change their behavior, they must be intrinsically motivated.

Zimmerman (1989) asserts that in order for students to be self-regulated learners, they must use specified strategies to achieve their goals. "Self-regulated learning strategies are actions and processes directed at acquiring information or skills that involve agency, purpose, and instrumentality perceptions by learners" (p. 329). Learning strategies should improve students' self-regulation of their "(a) personal functioning, (b) academic behavioral performance, and (c) learning environment" (p. 337).

Hypotheses. The following hypotheses were tested using multiple linear regression:

1. The motivational subscales which include intrinsic goal orientation, task value, and self-efficacy for learning and performance are positive predictors for success in a distance learning class.

2. The learning strategies subscales which include elaboration learning strategies, organization strategies, critical thinking strategies, and metacognitive self-regulation are positive predictors for success in a distance learning class.

3. The resource management subscales which include time and study management strategies, effort regulation strategies, and help-seeking strategies are positive predictors of success in a distance learning class.

Research Questions. The following research questions were answered using case study analysis:

1. What are the learning strategies that students in distance learning use when preparing for quizzes, discussions, and examinations?

2. What changes do students make in their learning strategies over the course of a semester?

3. Do the students perceive these changes in learning strategies as beneficial?

MATERIALS AND METHODS

Course Description. The biology course BSC 305, Evolution, was taught completely online by the authors of this article through University of Southern Mississippi using WebCT course management software during a typical, 15-week semester. This course had three major components for assessing student learning: quizzes, mandatory discussions, and examinations. The structure of the course required that students read a chapter and then take a five-question, multiple-choice quiz. Each quiz was open-book, but the students had only six minutes to answer the five questions which forced them to read carefully in preparation for the quiz. Responses made after six minutes were not accepted. Studying for the quiz prepared students for the asynchronous discussion of the chapter. We posted three discussion topics per chapter. The students were required to render five meaningful responses to these three topics which meant they had to respond not only to the posted topics, but to each other's comments. Early posting was important to the success of the asynchronous discussion, so we awarded one bonus point for each response made on the first day the topics were posted. Essay examinations were closely related to the discussion topic questions as each examination contained approximately six previously posted discussion topic questions drawn evenly from the chapters that had been covered during the examination period. Students were required to respond in more detail than had been required with the discussion topics.

Both the raw score and a letter grade to date were posted through WebCT throughout the semester to allow the students to keep up with their grades. The final letter grade was calculated as a percent of total points available using a ten-percentage point scale (i.e., 90% = A, 80% = B, etc.).

Quantitative Analysis 

Survey Instrument. The Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich et al., 1991) was used to gather quantitative data on the learning strategies and motivation of each student. The MSLQ "is a self-report instrument designed to assess college students' motivational orientations and their use of different learning strategies for a college course" (Pintrich et al., 1991; p. 3). It contains a total of 81 items divided into two sections, motivation and learning strategies.

The motivation section (31 items) is based on a broad social-cognitive model (Garcia and Pintrich, 1995; Pintrich and Smith, 1993). This section contains three general motivational constructs: (1) expectancy, (2) value, and (3) affect. (1) The subscales of self-efficacy and of control of learning beliefs measure the expectancy construct. (2) The value construct contains three subscales: intrinsic goal orientation, extrinsic goal orientation, and task value beliefs. (3) The third construct is affect which has one subscale, test anxiety.

Students were asked to complete all subscales, but only the subscales of intrinsic goal orientation, task value, and self-efficacy were used for our data analysis. This part of the MSLQ was administered at the beginning of the semester.

The learning strategy section (50 items) is based on Weinstein and Meyer's (1986) general cognitive model of learning and information processing (Garcia and Pintrich, 1995; Pintrich and Smith, 1993). This section is divided among three general types of scale: (1) cognitive, (2) metacognitive, and (3) resource management. (1) Cognitive strategies include both basic and complex strategies. Basic strategies are measured in a rehearsal subscale. Two subscales measure complex strategies of elaboration and organization. The elaboration strategies subscale includes strategies such as paraphrasing and summarizing. The second subscale, organization, measures complex learning strategies such as outlining and creating tables. A critical thinking subscale is also included in the cognitive scale. (2) Metacognition is measured by one subscale which is concerned with the learners' ability to control and regulate their own cognition. (3) The resource management scale contains four subscales which measure the learners' ability to control their own environment and includes strategies students use to control resources other than cognition including time management and study environment management, as well as the learner's interaction with peers and instructors.

Students were asked to complete all subscales, but we used only seven learning strategies subscales in the data analysis: elaboration, organization, critical thinking, metacognitive self-regulation, time and study environment, effort regulation, and help-seeking. The learning strategies section of the MSLQ was administered at the end of the semester.

Data Collection. Quantitative data were collected using both sections of the MSLQ. Permission to move the MSLQ to WebCT software was granted by Dr. Pintrich. The motivation section was administered at the beginning of the semester. Eight demographic questions were answered at the first part of this questionnaire in order to gather basic background information concerning students' gender, race, academic major, academic classification, whether they use their own computer, whether they live in the city where the course is taught, whether they have taken an online course before, and why they were taking the course. Students then answered the 31 questions on the motivation section of the MSLQ. This resulted in a total of 38 questions requiring 25 minutes or less to answer. The learning strategy section was administered at the end of the semester. These 50 questions took no longer than 30 minutes to answer. Both portions of the MSLQ were administered online.

Students answered the questionnaire on a voluntary basis, although extra credit was offered as a motivation. Students who completed the survey rated themselves on each item of the MSLQ (both the motivation and the learning strategies section) using a seven-point Likert-type scale with ranges from "not at all true of me" to "very true of me" (Pintrich, Smith, Garcia, and McKeachie, 1991). The scores for each question in a subscale were recorded, and an average for that subscale was calculated. For example, task value had six items. Each student's score was calculated by adding the score on each question in the subscale task value and then by determining the average of the six scores. Some questions are marked as "reverse" which indicates they are negatively worded. Scores for these questions were reversed before the average score was computed.

Qualitative Analysis 

Qualitative data were collected from four students during summer 2002 through three sets of email questions sent to the participants as the course progressed. The first set was distributed at the beginning of the semester, the second set was distributed in the middle of the semester, and the last set was distributed at the end of the semester. Data were also collected via a telephone interview at the end of the semester. A transcript of the telephone interview was sent to each student, and they were asked to make corrections or additions to the transcript. Three students returned the transcript with no corrections or additions. The fourth student did not return the transcript. Additionally, students were asked to fill out the learning strategies portion of the Motivated Strategies for Learning Questionnaire (MSLQ) at the beginning of the semester and again at the end of the semester.

Students were chosen for this study based on the following criteria: (1) they were a biology major, (2) they did well on the first quiz, (3) they were enrolled on the first day of class, and (4) this was their first distance learning class. The criteria were chosen because past experience in teaching the class indicated that those who meet these requirements were likely to complete the course. The fourth criterion was important to this study. Geographical diversity was added by choosing one student who lived in another state, one student who lived on the campus as the university where the course was taught, and two students who lived withing 90 miles of the university. The reason for choosing geographical diversity was to ensure that not all students had direct access to the instructor. In order to achieve this diversity, an exception was made to criterion one. The student that lived in another state was not a biology major.

Descriptions of the four students

Laura. Laura (pseudonym) was a white female who lived in a different state from the university where the course was taught. She was a senior and owned her own computer. She was majoring in a nonscience field and a course of this type was required of her major. Her final grade in this course was a C.

William. William (pseudonym) was a white male who lived on the campus of the university where the course was taught. He was a senior and owned his own computer. William was majoring in biology and was taking the course to fulfill the upper level (300/400) course requirements of his major. His final grade in the course was a B.

Janet. Janet (pseudonym) was a white female who lived approximately 90 miles from the university where the course was taught. She was a senior and owned her own computer. Janet was majoring in biology and was taking the course to fulfill the upper level (300/400) course requirements of her major. Her final grade in the course was a B.

Crystal. Crystal (pseudonym) was a white female who lived approximately 90 miles from the university where the course was taught. She was a senior and did not own her own computer. Crystal was majoring in biology and was taking the course to fulfill the upper level (300/400) course requirements of her major. Her final grade in the course was a B.

RESULTS

Quantitative Analysis

Demographics. A total of 54 students participated in the study, including 30 students who participated during spring 2002 and 24 students who participated during summer 2002. The class was largely female (67%) and Caucasian (83%). The majority of students (76%) were classified as seniors and 61% were biology majors. Most students (52%) had not taken a distance education course prior to this one and 81% owned their own computer. The principal reason for taking the class for 41% of students was that it fulfilled a requirement for an upper level (300/400) course. Table 1 shows the final grade distribution for the course. The mean score for the final grade was 3.56 (5 point scale where A = 5, F = 1) with a standard deviation of 1.40.

Tests of Hypotheses. The purpose of this part of the study was to determine if students' motivation and learning strategies could predict their final grades. Data collected using the Motivated Strategies Learning Questionnaire was correlated with final grades to test three hypotheses based on the structure of the MSLQ. This design used multiple linear regression in which correlation to final grades was the special case. Means for each subscale of the motivation section and the learning strategies section are found in Table 2. All variables met skew ness and kurtosis normal values. A statistical significance level of 0.05 was used. Descriptive and correlative techniques were used to analyze the data.

Hypothesis one: The motivational subscales which include intrinsic goal orientation, task value, and self-efficacy for learning and performance are positive predictors for success in a distance learning class. Multiple linear regression was used to determine if success was predicted by the independent variables of intrinsic goal orientation, task value, and self-efficacy for learning and performance. Pearson's correlations were used to describe associations between each of the motivational subscales and between each of the motivational subscales and grades (Table 3). Statistically significant correlations were found between grades and intrinsic goal orientation (r = 0.24), task value and intrinsic goal orientation (r = 0.60), self-efficacy and intrinsic goal orientation (r = 0.45), and self-efficacy and task value (r = 0.26).

Multiple linear regression was used to determine if the final grade (dependent variable) could be predicted by the predictors motivation subscales which include intrinsic goal orientation, task value, and self-efficacy for learning and performance. The regression [F(3,50) = 1.19, p = 0.32, [R.sup.2] = 0.067] was not statistically significant, and therefore the hypothesis was rejected.

Hypothesis two: The learning strategies subscales which include elaboration learning strategies, organization strategies, critical thinking strategies, and metacognitive self-regulation are positive predictors for success in a distance learning class. Pearson's correlations were used to describe associations between each of the learning strategies subscales and between each of the learning strategies subscales and grades (Table 4). Statistically significant correlations were found between grades and elaboration (r = 0.41), grades and critical thinking (r = 0.46), elaboration and organization (r = 0.45), elaboration and critical thinking (r = 0.41), elaboration and metacognitive self regulation (r = 0.52), organization and critical thinking (r = 0.28), organization and metacognitive self-regulation (r = 0.62), and metacognitive self-regulation and critical thinking (r = 0.45).

Multiple linear regression was used to determine if the final grade (dependent variable) could be predicted by learning strategies subscales which include elaboration, organization, critical thinking, and metacognition. The regression [F(4,49) = 1.87, p < 0.001, [R.sup.2] = 0.41] was statistically significant, and therefore the hypothesis was accepted. The subscales elaboration and critical thinking were positive predictors of final grades (Table 5). An [R.sup.2] value of 0.41 indicated that only 41% of the variance was explained. Organization was found to be a negative predictor of final grades. The mean for the B students was the highest and the mean for the A students the lowest.

Hypothesis three: The resource management subscales which include time and study management strategies, effort regulation strategies, and help-seeking strategies are positive predictors of success in a distance learning class. Pearson's correlations were used to describe associations between each of the resource management strategies subscales and between each of the resource management subscales and grades (Table 6). Statistically significant correlations were found between grades and time and study environment (r = 0.24), grades and effort regulation (r = 0.48), and time and study environment and effort regulation (r = 0.49).

Multiple linear regression was used to determine if the final grade (dependent variable) could be determined by resource management subscales which include time and study environment, effort regulation, and help-seeking. The regression [F(3,50) = 4.9, p = 0.005, [R.sup.2] = 0.23] was statistically significant, and therefore the hypothesis was accepted. Effort regulation was a positive predictor of the final grade (Table 7). The [R.sup.2] value was low (0.23) indicating the effort regulation explains 23% of the final grade.

Qualitative Analysis

The purpose of this qualitative research was to determine not only the learning strategies of students but also to determine if students changed their learning strategies over the semester and whether they felt these changes were important.

Four students participated in this aspect of the study. Three of the four were biology majors (the other student was majoring in a nonscience field), all did well on the first quiz, all were taking a distance education course for the first time, and all were enrolled in the course on the first day. One student lived on-campus, two students lived within 90 miles of the campus, and one student lived in another state. Three students made a B in the course and one student made a C.

Our first research question was, "What are the learning strategies that students in distance learning use when preparing for quizzes, discussions, and examinations?" The most important learning strategy reported by all students was organization. Organization includes strategies such as picking out important ideas and outlining the material to organize thoughts. Laura reported her grade fell because she was not able to read and outline the material as much as she did in the beginning of the semester. According to Laura, "It was real, really, really, useful to outline as I read." A quantitative aspect of the study conducted for the entire class (54 students) reported that organization was negatively correlated to grades. However the highest mean for organization was recorded for the B students and three of the four students who participated in the qualitative section of this study earned a B. William reported that he relied heavily on reading the material and on the instructor's notes. He stated, "I would usually reread what it was I didn't understand--just study it extra until I understood it." He did not organize by taking notes, but he would mentally organize the material. Laura also reported using an elaboration strategy. When she needed additional information she would go on-line to find out more about the topic. Elaboration strategies would include pulling together information from different sources. Crystal reported using a critical thinking strategy. "I participate in the discussions by reading what others write and agreeing or disagreeing." Janet would read and reread the material. She would also seek help form other sources such as the Internet. She would also read responses from other students to aid in understanding the material.

Our second research question was, "What changes do students make in their learning strategies over the course of a semester?" Laura's changes were the most significant. She was not able to keep up with the readings and outlining which caused her grade to drop. She finished the course with a C. Her scores on the MSLQ subscales supported this change. Her scores fell in all categories except effort regulation and help-seeking. The largest drop in her scores between the beginning of the semester and the end of the semester was in organization. While Janet said that she did not change her strategies, she also said that she began to keep a planner which helped her organize her activities. Her scores on the MSLQ changed very little with all scores changing less than 1.00. In the telephone interview, Crystal said she did not change her strategies. However, in the email, she stated that she began to reread the material. When she read the material several times, she would understand it better. Crystal's scores on the MSLQ subscales showed modest change. The largest gain was in the subscale of effort regulation and the largest drop was in the subscale of elaboration. Crystal reported using help from family members mainly at the beginning of the semester which correlated with her MSLQ scores in help-seeking that subsequently fell at the end of the semester. When asked directly, William repeatedly stated he did not change his learning strategies during the semester, but he reported in the telephone interview that he started using the professor's notes at the end of the semester and "then going back and being sure to cover in the chapter what he covered." The changes in his MSLQ scores were slight. The largest change was in the category of effort regulation in which his score increased by 2.00. His time and study environment scores decreased by 1.58.

Our third research question was, "Do the students perceive these changes in learning strategies beneficial?" Laura reported the changes she made were not beneficial. She was not able to organize the material which lead to her lower grade in the course. She told us that she mainly sought help from her family at the beginning of the semester. She did not say whether this was beneficial but she did not use her family as much toward the end of the semester. Janet began keeping a planner with helped her organize her time and study environment which she found to be beneficial. Crystal and William reported that they did not change their learning strategies over the semester. But as we have noted, William began to use the professor's notes and then reviewing the chapter again. He did this toward the end of the semester and he stated it was the most beneficial preparation.

By the end of the semester, all four students scored above a four on the seven point metacognitive self-regulation subscale (the scale ranged from one to seven). Questions that addressed what the students did when they encountered an obstacle were aimed at discovering metacognitive self-regulation strategies. Most would read other students postings when they did not understand a concept. William would read the material until he understood it. Crystal would discuss the material with family members.

All but William indicated that they used help-seeking strategies. William's score was low on the subscale help-seeking, and he did not report seeking help from others. Laura, Crystal, and Janet stated that they would read other students' postings to help them understand the material. Crystal also asked family members to help. She stated, "I will read out loud to someone in my house and discussion will get started between the others in the house. That seems to help a lot." In the third set of email questions, she said that she mainly used family members at the beginning of the semester. This correlates with her MSLQ score (help-seeking subscale) of 5.5 at the beginning of the semester and then at the end (3.75). Laura reported an increased use of help-seeking which would coincide with the difficulties she had with the course. Even though Janet reported that she sought help from others, her scores on the MSLQ help-seeking subscale were low at the beginning of the semester (2.75) and at the end (3.0).

Janet, Crystal, and Laura reported difficulty with time and study environment. Janet had a difficult time organizing her time around other activities. She stated keeping a daily planner to help overcome this. Crystal reported having difficulty finding quiet time. Laura reported difficulty completing assignments around her morning sickness. By the end of the semester, she was managing her time better and enjoyed the distance education format. Laura scored above a four on the time and study environment subscale both at the beginning and at the end of the semester. Crystal and Janet scored the highest on this subscale. William reported that he did not have any problem with time and study environment, but his subscale score was a 3.37 at the end of the semester This does not correlate with his statements concerning time and study environment. This was his second lowest score on the MLSQ learning strategies subscale.

DISCUSSION

Quantitative Analysis. The purpose of this research was to discover the degree to which motivation and learning strategies affected the success of students in distance education. Pintrich (1995) explained that successful students were self-regulated. They are able to monitor and control their learning and not depend on someone else for their success. They should also be self-motivated.

Hypothesis one suggested that the motivational subscales which include intrinsic goal orientation, task value, and self-efficacy for learning and performance were positive predictors for success in our distance learning class. Self-motivation would include intrinsic goal orientation, placing value on the task, and having self-efficacy. The students in this study were intrinsically motivated, valued the task, and had high self-efficacy at the beginning of the semester. However, some were not successful in the course. One possible explanation is that motivation was measured at the beginning of the semester and compared to the final grade. Changes in motivation may have occurred for those who were not successful.

Hypothesis two suggested that the learning strategies subscales which include elaboration learning strategies, organization strategies, critical thinking strategies, and metacognitive self-regulation were positive predictors for success in a distance learning class. This proposal was supported by our regression analysis. The subscales elaboration and critical thinking were positive predictors of success while organization was a negative predictor. Elaboration strategies include strategies that "help the learner integrate and connect new information with prior knowledge" (Pintrich et al., 1991; p. 20). Olgren (1998) suggested that in order for students to construct knowledge, they must be able to integrate knowledge. Olgren goes on to state that learning involves actively searching for understanding. To understand and construct knowledge, students relate the new knowledge to prior experiences. In order to do this, students must use elaboration strategies. This would be very important in a course designed with elaborate discussions as BSC 305 was where students had to apply their knowledge. Critical thinking would also be very important. Critical thinking strategies involve the application by students of previous knowledge to new information which would allow them to solve problems (Pintrich et al., 1991).

Curiously, organization was not found to be a positive predictor of success which is contrary to indications from educational literature. Doljanac (1994), for example, investigated whether motivation and learning strategies observed of college students could predict academic performance which was measured by the grade earned in the course. In this study we see that higher grades are correlated with higher rates of the cognitive learning strategies which include the subscales of rehearsal, elaboration, and organization.

We propose possible explanations for the results of organizational skill. (1) The MSLQ is a self-report instrument. Students who made the lower grades may have perceived that they were organized. Those who made a B in the class had the highest mean for organization and those who made an F had the next highest mean. The students who made an A in the class had the lowest mean, and these highest achievers may never feel that they are sufficiently organized. (2) Alternatively the format of the class was such that students had to understand and apply the material. Organization involves organizing the material to study. A student can be well organized in other respects measured by MSLQ, but not understand the material.

Other studies have reported that the implementation of learning strategies is important to be successful. Shih (1998) conducted a study in which correlations between achievement in a web-based course and the variables of motivation and learning strategies were analyzed. Shih collected data with a self-developed on-line questionnaire which included the scales of motivation, learning strategies, patterns of learning, and attitude scales. The results of this study indicated that the two most important variables used to determine achievement are motivation and learning strategies.

Hypothesis three suggested that the resource management subscales which included time and study management strategies, effort regulation strategies, and help-seeking strategies were positive predictors of success in a distance learning class. The regression was statistically significant with the subscale of effort regulation being a positive predictor of success. Effort regulation is important in a distance education course, where the more obvious deadlines of coming to class and taking examinations in class are absent. Help-seeking had a low overall mean indicating students were not asking for help. This may be inherent in an online environment where students have no face-to-face contact with the instructor or other students. They may have perceived that they were not able to ask for help.

Qualitative Analysis. Students who participated in the qualitative study reported that the main strategy they used was organization. Three of the four students in the qualitative study earned a B in the course, and the highest mean for organization in our quantitative study was for B students. The types of organization these four students reported apparently was insufficient to help them understand and apply their knowledge of the material to earn an A.

CONCLUSIONS

This study contributes information on the learning strategies and motivation of successful students in distance education. The students who participated in this study were highly motivated at the beginning of the semester, but motivation was not a positive predictor of grades. Elaboration, critical thinking, and effort regulation were found to be positive predictors of success while organization was a negative predictor of success. Zimmerman and Pons (1986) states that self regulated learning is correlated to achievement with 93% accuracy. However, this study did not find that to be true. The participants do not appear to be self-regulated, but were successful. In fact, the grade distribution was negatively skewed which means the distribution curve indicated that the class mean was higher toward the for higher grades.

Students who participated in the qualitative study reported that the main strategy they used was organization. The students in the qualitative study earned a B in the course and the highest mean for organization in the quantitative study was for B students. Perhaps this type of organization is not enough to help students understand and apply their knowledge of the material. The students in the qualitative section also sought help from others (mainly others in the class) but help-seeking was not statistically significant in the quantitative study. They would read other students' material, but they may not have considered this asking for help.

According to Zimmerman (1986), learning is not something that can be done for students, rather it is something that is done by them" (p. 22). Students who are self-regulated take charge of their learning and become active participants. Pintrich (1995) states college students have control over their time management and study schedule but they may have difficulty managing the freedom as the quality of cognitive effort they put into mastering academic goals. Students will adapt better and are more likely to succeed if they can learn to control their learning. Because of this, research on self-regulation is especially important for college students enrolled in distance education courses. The students in the qualitative study show some evidence of being self-regulated learners. All used cognitive strategies to master the material and when they did not understand the material, they were aware of this and made an effort to master the concepts. Additionally, they scored above a 3.5 on most subscales. The exception was help-seeking, but some of the students in the study indicated that they did seek help from either their peers or from family members. They may not have realized that reading other students' postings in order to better understand that material is a help-seeking strategy.

Distance educators must become skillful in supporting learners and disseminate information. In order to do this, we must know what methods the students use to learn and how are they motivated. Students of distance education should be highly motivated and self-monitoring. They should also know the cognitive skills needed to succeed. These are aspects of self-regulation. If it is important for college students to be self regulated as stated by Pintrich (1995), then it should even more important that students of distance education be self-regulated.

More students are enrolling in distance education courses. Moore (1998) says that the ability to link students and teachers through technology has never been so powerful. Gibson (1998) states "Yes, the students will come, but one has to ask, will they succeed (p. viii)?" For students to be successful in distance education, it is important to understand how learning occurs and what influences learning (Olgren, 1998). If instructors know what learning strategies and motivational factors are important, then perhaps these instructors can help students in distance education succeed. This information should be disseminated to students who enroll in distance education courses. Granger and Benke (1998) explain that one way instructors can help students succeed is to provide orientation that presents methods that will help students of distance education. According to this researcher's study, information on organizing material, along with critical thinking skills and elaboration skills could be included in the orientation. Students should also be aware of how important effort management is in distance education. This information could be delivered via videotapes or the Internet. Based on the results of this research, students need to integrate elaboration strategies and critical thinking strategies while managing their effort. Students must be aware of the strategies such of organizing and integrating information in order to construct knowledge. Olgren goes on to state "Cognitive strategies cannot be divorced from the learnerr's [sic] purpose in using them" (p. 84). As a result, students must also be motivated to learn. Only by helping students of distance education discover learning strategies and motivating them to learn can instructors help them become successful distance learners.

LITERATURE CITED

Doljanac, R.F. 1994. Using motivational factors and learning strategies to predict academic success. Dissertation Abstracts International, 59(1):0142A, (UMI No. 9513340).

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Beth Dunigan (1) and Kenneth J. Curry

Mississippi College, Clinton, MS 39058, and University of Southern Mississippi, Hattiesburg, MS 39406

Beth Dunigan Corresponding Author: Department of Biology, Box 4045, 200 South Capital; 601-925-3972; email: [e-mail] 

Table 1. Grade distribution (N = 54).

Grade  Number  Percent

A      15      18
B      21      25
C       6       7
D       3       4
F       9      11

Table 2. Descriptive Data (N = 54).

                               M    SD

Intrinsic Goal Orientation     5.3  1.0
Task Value                     5.7  1.2
Self Efficacy                  5.9  0.9
Elaboration                    4.8  1.1
Organization                   3.7  1.2
Critical Thinking              4.8  1.0
Metacognitive Self-Regulation  4.4  0.7
Time and Study Environment     5.0  1.1
Effort Regulation              5.5  0.8

Table 3. Correlations for Grades and Motivation Subscales (N = 54).

                                                 Intrinsic Goal  Task
                                         Grades  Orientation     Value

Pearson      Intrinsic Goal Orientation  0.24*
Correlation  Task Value                  0.14    0.60*
             Self Efficacy               0.20    0.45*           0.26*

* p < 0.05

Table 4. Correlations Between Learning Strategies and Grades (N = 54).

                                                               Critical
                            Grades  Elaboration  Organization  Thinking

Pearson      Elaboration     0.41*
Correlation  Organization   -0.12   0.45*
             Critical        0.46*  0.41*        0.28*
             Thinking
             Metacognitive   0.18   0.52*        0.62*         0.45*
             Self-
             Regulation

*p < 0.05

Table 5. Coefficients for Learning Strategies (N = 54).

                               Standardized Beta
Model                          Coefficients       p

Elaboration                     0.41              0.004
Organization                   -0.44              0.003
Critical Thinking               0.39              0.004
Metacognitive Self-Regulation   0.06              0.730

Table 6. Correlations for Resource Management (N = 54).

                                     Time and  Effort
                             Grades  Study     Regulation  Help-Seeking

Pearson      Time and Study   0.24*
Correlation  Effort           0.48*   0.49*
             Regulation
             Help-seeking    -0.02   -0.07     -0.02

*p < 0.05

Table 7. Coefficients for Resource Management.

                            Standardized Beta
Model                       Coefficients       p

Time and Study Environment   0.010             0.940
Effort Regulation            0.470             0.002*
Help-Seeking                -0.006             0.960

*p < 0.05