The Self-Regulated Learner Advantage:
Learning Science on the Internet
by
Jace Hargis, Ph.D.
University of North Florida
Science educators recognize the potential of the Internet as an educational tool. However, little research based on educational theory and self-regulated learning has been conducted to determine how educators can maximize the learning potential of this relatively new teaching tool. One of the major aspects of this study was designed to illustrate the need for self-regulated learning when using the Internet for education. Two alternate forms of an on-line instructional web site containing the same information were developed. The first, a constructivist format provided more links for students to wander and build concepts with the material in ways that may be consistent with their particular learning style. The second is called an objectivist format and is similar to presentations found in academic settings where lectures are provided.
This on-line study examined the effect of variables such as age, gender, racial identify, attitude, aptitude, self-regulated learning and self-efficacy on learning. However, the purpose of this paper is to introduce and examine the effect of self-regulated learning on the Internet. The results of this study indicated that typical learner characteristics were not roadblocks to on-line learning. However, the study group consisted of post-secondary science and engineering majors using chemistry content-oriented web pages. The results did demonstrate that all participants were able to significantly increase their scores between the pre- and post-assessments after completing the on-line instructional module. Self-regulated learning ability was measured and practically all participants scored high on this parameter. This indicates that most technically-oriented college students either inherently possess the ability to regulate their own learning or they have integrated this characteristic into their tool set in order to become successful science and engineering students. In either case, the ability to self-regulate one's learning appears to be an ideal characteristic for learning on-line via the Internet. This parameter may also be able to filter out individuals who may not be successful participating in Internet or distance learning courses, which are becoming popular today.
Why Should We Use the Internet?
There are many reasons for using the Internet. Some of the reasons include a higher equity of access, an infinite resource, students as active participants, motivational influence of authentic learning activities, student inquiry and cooperative learning, and improved assessment of student progress. Technology has gained attention in education today because of its prevalence; its promise to provide low cost education; and it may help some people to participate more easily, to learn more effectively, and to enjoy learning more (Palmieri, 1997). Forman (1987) indicates that technology adds the ability for students to choose how, when, and where they participate in the learning experience and to bring together a vast wealth of previously unavailable learning resources. It has been reported (Comber, 1997) that children using computers focused on tasks for longer periods; found previously boring tasks more interesting; were more eager to participate in and contribute to discussions; asked more questions; and improved their use of the standard conventions of print. There is substantial evidence to suggest that the computer also offers the advantage of making work more stimulating, thereby motivating the individual. The search for information is made considerably easier, thus making one's workload less tedious, and perhaps more interesting.
Given adequate access to technology, the Internet can provide both teachers and students with an ever-growing resource of information. Used effectively, this environment has the potential to level the playing field for individual differences that have historically provided obstacles to learning. However, if the student does not have the appropriate support or they are not able to regulate their own learning with some amount of structure and discipline, all of these positive attributes will transform into impassable barriers. In addition, it is a mistake to emphasize connecting schools to the Internet without considering the kinds of thinking processes students need in order to learn from the information they access.
What Is Self-Regulated Learning?
Corno and Mandinach (1983) have defined self-regulated learning as an effort to deepen and manipulate the associative network in a particular area and to monitor and improve that deepening process. It refers to the deliberate planning and monitoring of the cognitive and affective processes that are involved in the successful completion of academic tasks. Bandura (1977) defined self-regulation, as the ability to control our own behavior and it is the workhorse of human personality. Bandura suggests three steps: (1) Self-observation, we look at ourselves, our behavior, and keep tabs on it; (2) Judgment, we compare what we see with a standard; (3) Self-response, if we did well in comparison with our standard, we give ourselves rewarding self-responses. If we did poorly, we give ourselves punishing self-responses. Strategies include self evaluation, organizing and transforming, goal-setting and planning, seeking information, keeping records and monitoring, environmental structuring, self consequating, rehearsing and memorizing, seeking social assistance, and reviewing records (Zimmerman, 1989). Yang (1993) has found that with respect to self-regulatory learners: 1) high regulatory students tend to learn better under learner control than program control; 2) high self regulatory students are able to monitor, evaluate, or manage their learning effectively during learner controlled instruction with embedded questions; 3) learner control reduces instructional time required to complete the lesson; and 4) high self-regulatory students manage their learning and time efficiently.
There are several characteristics of computer technology that make it a desirable vehicle for examining the concept of self-regulated learning. Self-regulated learning is not a mental ability, such as intelligence, or an academic skill, such as reading proficiency; rather, it is the self-directive process through which learners transform their mental abilities into academic skills (Schunk & Zimmerman, 1998). Computers make it possible to independently store data collected via interaction with the student thus providing the possibility for following student moves as a source of data and later providing feedback to them. This capability has instructional benefits: first, learner interaction with concepts can be stored and retrieved for later analysis; and second, the immediate feedback that the learner receives allows a greater degree of learner control by providing individualized opportunities for review. McDonald and Ingvarson (1995) found that independent learning of this type has a strong chance of success due to the extended resources that the computer offers. Theoreticians seem to agree that the most effective learners are self-regulating (Zimmerman, 1996; Winne, 1995). In an academic context, self-regulation is a style of engaging with tasks in which students exercise a suite of powerful skills: setting goals for upgrading knowledge; developing strategies; and, as steps are taken and the task evolves, monitoring the accumulating effects of their engagement. As these events unfold, obstacles may be encountered. It may become necessary for self-regulating learners to adjust or even abandon initial goals, to manage motivation, and to adapt and occasionally invent tactics for making progress. Self-regulated students are thus aware of qualities of their own knowledge, beliefs, motivation, and cognitive processing. This awareness provides grounds on which the students judge how well unfolding cognitive engagement matches the standards they set for successful learning (Corno, 1994; Howard-Rose & Winne, 1993; Zimmerman, 1989). Remember if you give a man a fish, you feed him for a day. If you teach a man to fish, you feed him for a lifetime (Confucius, 551-479 B. C.). One of the best reasons why the Internet should be used in educational settings follows a similar pattern. If you give a person a computer, you frustrate them for a lifetime. If you teach them how to use the computer and the Internet, you empower them.
On-Line Instrumentation and Approach
A study was conducted using computers equipped with Internet access in various laboratories, homes and businesses on and around a major southern U.S. university. To ensure equal access to all participants, the university maintains several computer laboratories with adequate hardware configurations and software applications to support Internet connections to all students. The participants included one hundred and forty-five volunteer post-secondary students from the Engineering Research Center and the College of Engineering. Eighty-four males and sixty-one females were equally distributed between each learning format. The age of study participants ranged from eighteen to thirty with a mean value of twenty-two. Participants were randomly assigned to one of two treatment groups or a control (Table 1). The design is a pre-assessment/post-assessment with a control group.
Table 1. Study Design.
| Attitude, Verbal, and Self-Regulation/ Efficacy |
Group Assignment | Pre- Assessment |
Treatment #1 Constructivism | Treatment #2 Objectivism | Post- Assessment |
| O | R (n=68) | O | X1 | O | |
| O | R (n=65) | O | X2 | O | |
| O | R (n=12) | O | O |
The independent variables used for this study are gender, age, racial identity, attitude, self-regulated learning and aptitude, although only self-regulated learning approach will be discussed. Self-regulated learning was measure on an ordinal scale with an eighty-one item, 5-point Likert scale questionnaire called the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich & Garcia, 1993). The MSLQ is a self-report instrument designed to assess college student's motivational orientations and their use of different learning strategies and is based on a general cognitive view of motivation and learning strategies. A one on the scale represented when the statement was not true at all of the participant and a seven would indicate when it was true of them. There are essentially two sections to the MSLQ, a motivated section and a learning section. The motivated section consists of 31 items that assess students' goals and value beliefs and their anxiety. The learning strategy section includes 31 items regarding students' use of different cognitive and metacognitive strategies. In addition, this section includes 19 items concerning the student's management of different resources. Previous results have shown that the MSLQ scale is to be a reliable and valid measure of self-regulated learning.
The pre- and post-assessment administered were the same in content and format. In addition to a general chemistry section, all participants were provided a criterion referenced written pre-assessment. All questions were based on the concepts presented in the web-based instruction modules. Acceptable split-half reliability was calculated using the Spearman-Brown formula. The assessments consisted of two subsections. The initial subsection measures basic general chemistry knowledge and the second focused on the specific information that the module presented. A combination of both forced and free choice items were used for the assessments. A true constructivist treatment on the Internet would allow the user to access all areas and roam freely. However, in this study, that was impractical. Therefore a constructivist environment was operationally defined as one, which allows the participant to access several internal links to build their knowledge of the subject. The objectivist treatment material was presented in a prescribed linear order. The objectivist module allows the participant to examine the same textual information. However, after the completion of a screen, they are presented with singular link which forwards to the subsequent page of text. The participant is allowed to move backward to a previous page to review previous topics.
Results
Out of the 289 students that participated in the study, 145 science and engineering majors completed all of the required forms on-line after completing either a constructivist or objectivist instruction module or the absence of a module, which was used as a control. Significant drop out rates were anticipated since students were not monitored during the procedure and could terminate their participation at any time. This issue should be considered whenever on-line studies are performed. The results are tabulated below.
Table 3
Motivated Strategies and Learning Questionnaire Descriptive Statistics
| n | Mean | SD | Variance | Range | Possible | |
| Self-Regulated/ Self-Efficacy | 145 | 4.71 | 1.64 | 2.83 | 1-7 | 7 |
| Post-scores | 145 | 6.3 | 3.1 | 9.8 | 0-10 | 10 |
Table 4
Motivated Strategies and Learning Questionnaire Reliability Data.
| Parameter | Result |
| Cronbachs Alpha | .9177 |
| Split Half | .9410 |
| Spit Half (w/Spearman) | .9696 |
There was no significant difference between MSLQ parameters and post-assessment scores in this study. The ANCOVA (Table 5) results indicate that there was not a significant difference at = .05 between post-assessment scores and the self-regulated learning/self-efficacy scores, thus hypothesis five would be accepted.
Table 5
ANCOVA Source Table for Post-Assessment vs. Self-Regulated Learners/Self-Efficacy
Dependent Variable: Post-Assessment
| Source | DF | Type III SS | Mean Square | F Value | Pr > F |
| Pre | 1 | 334.515023 | 334.515023 | 59.57 | 0.0001 |
| Group | 2 | 18.083963 | 9.041981 | 1.61 | 0.2038 |
| Self-Regulated /Self-Efficacy | 1 | 0.487494 | 0.487494 | 0.09 | 0.7687 |
| Self-Regulated /Self-Efficacy *Group | 2 | 1.207739 | 0.603870 | 0.11 | 0.8981 |
| Parameter | Estimate | T for Ho: Parameter=0 |
Pr >/T/ | Std Err of Estimate | |
| Intercept | -1.438535010 | -0.16 | 0.8766 | 9.24716711 | |
| Pre- Assessment |
0.687603518 | 7.72 | 0.0001 | 0.08909053 |
Conclusion and Implications
When science instruction is presented in linear and non-linear formats to science and engineering post-secondary students via the Internet, there are no significant differences in their post-assessment scores. An analysis of covariance (ANCOVA) with general linear model procedures was performed comparing the format with participant post-assessment scores. There was a significant difference at = .05 between groups on post-assessment scores. However, the major difference occurred between the groups with instruction and the control. A follow-up procedure demonstrated that there was not a significant difference between the two instructional module groups on the post-assessment scores, thus either approach allowed the participant to learn.
In addition, statistical comparing each variable to the instructional formats produced similar non-significant differences with one exception. There was no difference between self-regulated learners and post-assessment scores. However, all of the participants had tested high for self-regulating abilities and subsequently performed well on the on-line assessment. The study was designed for specific application for technically-oriented post-secondary students and their performance using the Internet for instructional purposes. Although this approach produced results for this sampling group, it does not allow a broader generalization to a less technical group or one with lower self-regulating abilities. Although further work will need to be performed to completely answer the question of the global effects for self-regulated learning, the preliminary results would indicate that the better a student is at regulating their own learning, the higher their chances of success while learning on the Internet. Prior literature also supports the outcomes of this study indicating a positive relationship between individuals who have the ability to regulate their own learning and knowledge acquisition or achievement.
References
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About the author...
Dr. Jace Hargis currently teaches science and technology courses at the University of North Florida in Jacksonville, FL. In addition, he instructs an online course integrating technology into science curricula for the University of San Diego in San Diego, CA. Dr. Hargis' examines how students learn on the Internet through an interactive webpage at
http://www.jhargis.com. Finally, he works as a consultant for SmartKidZone (http://www.smartkidzone.com) based in Jacksonville, FL.