Annotated Bibliography_1
Bulger, M. E., Mayer, R. E., Almeroth, K. C., & Blau, S. D. (2008). Measuring learner engagement in computer-equipped college classrooms. Journal of Educational Multimedia and Hypermedia, 17(2), 129-143.
Although engagement and learning appear linked, quantitatively measuring this relationship is challenging. New technologies offer a window into studying the interactions among classroom activity, student engagement, and positive learning outcomes in computer-equipped classrooms. Early studies defined engagement in terms of interest, effort, motivation, and time on task. Research on active engagement consistently shows that when students are focused on a task, they are more likely to apply effort during their learning experience. A trend in recent research is to study the cognitive strategies that result from varying levels of motivation. Meta-cognitive control, which is evident in students' ability to not only know what to do in a learning situation (cognitive strategies), but when to do it, is measured by self-efficacy cues, self-regulation, and goal setting. Students demonstrating cognitive strategies such as task-mastery goals report higher levels of engagement and perform better on assigned tasks. In light of the new measurement opportunities made possible by emerging technologies, it makes sense to return to Berliner's research (1987,1990) that assumes a causal relationship between engaged time that is the period of time in which students are completely focused on and participating in the learning task and academic achievement.
In this research, researcher applied Berliner's concept of time on task to measure student engagement levels. Researcher use Classroom Behavioral Analysis System (CBAS) to measure student engagement in a college writing class. Student computer-based behaviors (off-task and on-task internet visits) were compared during a traditional, lecture-based lesson (no-simulation condition) and an interactive simulation-based lesson (simulation condition). There are two purposes of this study, namely: (a) to compare student engagement during lecture taught using computer compared with lecture using traditional methods, (b) to measure student engagement levels affected by instructional style. There are three research questions: (a) how engaged are students during a lecture taught using traditional methods? (b) how engaged are students during a lecture taught using a simulation exercise? (c) are student engagement levels affected by instructional style?
The participants were 139 students enrolled in freshman composition courses at the University of California, Santa Barbara. Out of 144 students, 139 volunteered for the study and five chose not to participate. All consented to the recording of their in-class computer activities. Thirty-two participants in two intact classes were given the no-simulation treatment and 107 participants in five intact classes were given the simulation treatment.
The design is quasi-experimental because intact classes (rather than individual learners) were assigned to the treatments. The dependent variable was student engagement as measured by the number of off-task and on-task internet activities during a class lesson. Off-task internet activities operationalized as website visits that were not part of the assigned class activity. On-task internet activities included website visits that related to the assigned class activity, such as a word definition search or the use of an online writing lab. CBAS installed on each computer and recorded keystroke activities, active applications, and URL visits. A video camera positioned in the back of the classroom recorded observable classroom activity, including the instructor's actions and participant behavior. Participants observed during a single 110-minute instructional episode. In the two non-simulation classes, the instructor used a traditional, lecture-style format for the first 15 minutes of class and then directed the students to use the additional class time to revise their paper drafts. For the five simulation classes, researcher developed a simulation exercise consisting of a website that detailed a mining accident and prompted participants to write a rescue plan. In these classes, the activity took place in real time. Participants worked collaboratively in groups while the instructor participated directly in the learning activity by role-playing and responding to student requests for information and support.
The results: In the no-simulation condition, participants performed significantly more off-task internet actions (M=34.31, SD=28.03) than on-task internet actions (M=11.72, SD=11.33), t (31) = 4.35, p< .001. Off-task internet actions accounted for 79% of the cohort's total internet use. This result shows that a lesson taught using a traditional lecture-style format that did not apply engagement research findings resulted in low student engagement levels, as reflected by high off-task internet actions. In the simulation condition, participants performed significantly more on-task internet actions (M=27.71, SD=19.11) than off-task internet actions (M=3.79, SD=5.89), t (106)=12.55, p< .001. Off-task internet actions accounted for 9% of this cohort's total internet use. This result shows that using an interactive simulation exercise resulted in increased student engagement levels, as reflected by high on-task internet actions. Base on student engagement levels affected by instructional style, researcher finding that students in the no-simulation condition appear to have lower engagement levels than students in the simulation condition. These results show that it is possible to specifically design an instructional episode that heightens student engagement levels. This study shows that student engagement is related to instructional method, namely, that the no-simulation condition primed lower engagement in learners than did the simulation condition. Researchers found that it is possible to encourage high levels of student engagement by using an interactive simulation exercise. The high levels of student on-task actions in the simulation classes indicate that directed interactive activities can promote high levels of student engagement.
The limitations of the study were: First, researcher did not explain the instructors. I think different instructors taught in the two conditions will make the research bias. Second, in the simulation condition, participants worked in groups, whereas in the no-simulation condition, participants worked alone, researcher not consistently requiring group work in both conditions. Third, the sample size for the no-simulation condition was considerably smaller than the simulation condition. Values of the research to our group project are as follow. This research gives us insight how to measure student engagement. In a typical classroom situation in Indonesia, some students are paying attention to varying degrees and others are not. It is difficult for teacher to determine the extent to which students are actually engaged with the classroom activities. Behavioral cues, such as students looking at the teacher, may provide some indication of engagement levels; however, students who appear to not be paying attention may be completely engaged and vice-versa. This research gives another example using technology to measuring student engagement. This research also be a model to my group, how to design quasi-experimental research.***agepe
0 komentar:
Posting Komentar