1 Self-regulated Learning Strategies and Achievement in an Introduction to Information Systems Course Catherine, S. C. (2002). Self- regulated learning strategies and achievement in an introduction to information systems course. Information Technology, Learning, and Performance Journal, 20(1), Instructor: Chen, Ming-Puu Presenter: Tsai, Yu-Ting

2 Introduction Learners are no longer viewed as passively being ‘instilled’ with information and knowledge; they are actively involved in reorganizing and reconstructing their existing knowledge with new knowledge (Perkins, 1992). Understanding the concept of self-regulation is important in the development of these achievement capabilities for both teachers and students. Self-regulated learning is a self-initiated action that involves goal setting and regulating one’s efforts to reach the goal, self- monitoring (metacognition), time management, and physical and social environment regulation (Zimmerman & Risemberg, 1997).

3 Purpose of the Study The purpose of this study was to identify the effective self-regulated learning strategies in a lecture and in a hands-on computer lab learning environment of an information systems course. This study investigated the effect of students’ prior computer experience and software used in these two types of learning environments.

4 Review of literature Learning Environments in a Information Systems Course The goal of this first information systems class is to prepare students to use the acquired computer knowledge and skills in other upper-division business classes, such as accounting, finance, and management (Lippert & Granger, 1998). Since the goal is for students to acquire both computer knowledge and software skills, this course usually consists of lectures and hands-on computer labs.

5 Review of literature Learning Environments in a Information Systems Course A lecture environment requires students to listen, to take notes, and to take pencil-and-paper exams. A lab environment requires students to follow instructions to perform tasks on the computer. These two types of learning are different, and different learning situations may require different learning strategies to be successful (Weinstein & Mayer, 1986).

6 Review of literature Self-regulated Learning Zimmerman (1989), self-regulated learners are individuals who are “metacognitively, motivationally, and behaviorally active participants in their own learning process.” A main feature of self-regulated learning is metacognition. Metacognition refers to the awareness, knowledge, and control of cognition; the three processes that make up metacognitive self-regulatory activities are planning, monitoring, and regulating (Pintrich et al., 1991).

7 Review of literature Self-regulated Learning Other aspects of self-regulated learning include time management, regulating one’s own physical and social environment, and the ability to control one’s effort and attention (Pintrich, 1995; Zimmerman & Risemberg, 1997). Research has revealed that high achievers reported more use of self-regulated learning strategies than lower achieving students (Pintrich & DeGroot, 1990; VanZile- Tamsen & Livingston, 1999).

8 Review of literature Self-regulated Learning Students learn self-regulation through experience and self-reflection (Pintrich, 1995). Teachers can teach in ways that help students become selfregulating learners (Coppola, 1995; McCombs, 1989). Self-regulated learning is particularly appropriate for college students, as they have great control over their own time schedule, and how they approach their studying and learning (Pintrich, 1995).

9 Method-Research Instruments To collect data on demographics and prior computer skills, students were administered a demographic survey at the beginning of the semester. Two weeks before the final examination, students were administered the MSLQ to assess their self-regulated learning strategies used during the semester.

10 Method-Research Instruments Subjects ─ 197 students in a business information systems course Gender and College ─ female(42.6%), male(57.4%) business(70.1 ％ ), sciences and humanities(12.7 ％ ), architecture, fine arts, teachers college, and CIM(14.7% ) Few students who had not decided their majors.

11 Method-Research Instruments Year in School and Course Requirement ─ sophomores(57.9 %), freshmen(16.2%), juniors(17.8%), seniors(7.1%). Prior Software Work Experience and Software Courses Taken ─ Question1 about experience working with software. (no experience, 1-4 months, 5 months to 1 year, and 2 or more years) Question2 about experience software courses (none, 1 course, and 2 or more courses)

12 Method-Research Instruments MSLQ Instrument ─ 5 scales ： (1) metacognitive self-regulation (2) time and study environment (3) effort regulation (4) peer learning (5) help seeking Responses were scored using a 7-point Likert type scale, from 1 (not at all true of me) to 7 (very true of me).

13 MSLQ

14 Findings

15 Findings β <0.1 → Table4p <.01 R 2 =.229

16 Findings R 2 =.236

17 Findings

18 Findings

19 Findings R 2 =.069

20 Findings R 2 =.076

21 Findings

22 Conclusion and Recommendations (1)Students who studied computer concepts with peers achieved lower test scores. Prior computer experience did not help students achieve higher test scores. →This group of students has had experience in using computers. They probably has more other social activities to distract them from studying. →Instructors would be to encourage students to control their effort and attention when facing distractions and uninteresting tasks.

23 Conclusion and Recommendations (2) The use of somewhat outdated software in teaching led students to have higher test scores. →One hypothesis would be that this group of students was more familiar with current software since they started using computers only recently, unlike returning students who might have been using software on the job for a long time. →When not-so-new software was used in teaching, it might have prompted students to study harder because of its unfamiliarity. →Data will need to be collected from students with the same demographic characteristics to either confirm or reject the effect of software used on achievement.

24 Conclusion and Recommendations (3) Lab assignments ─ the normal probability plot indicated that the data collected did not meet the assumption of normality. →Research is needed to collect data from the same population with a larger sample size and multiple four- year institutions to test this hypothesis. →The MSLQ instrument was not an appropriate instrument to assess effective learning strategies. →To gather learning strategies from students and then to investigate which of those strategies lead to achievement.