Increasing Student Achievement Through Engaging, Technology-Mediated
Inquiry-Based Science Learning

Purpose

The purpose of my study is to determine the relative effectiveness of two types of instruction – innovative direct instruction and hands-on guided inquiry – on student achievement in middle-school science and technology classrooms.

Conceptual Framework

Although Simon (2001) reaffirmed that "learning takes place inside the learner and only inside the learner" (p. 210), I believe that the learning process is nurtured through collaboration, dialogue and active engagement. To foster such a nurturing learning environment and student-centered instruction in my science and technology classrooms, I designed my instruction and structured the classroom so students work in teams on authentic and challenging, yet fun problems. Even as they built and tested their creations and attempted to improve their product's performance, questions were spontaneously generated. As the subject-matter expert in the classroom, it was now easier for me to seize these teachable moments and help my students think through their designs, carry out their investigations, and answer their own questions. Through facilitating such guided inquiry in the classroom and reflecting on my own learning, I recognized the importance of both motivational and cognitive elements in this adaptive process (Balasubramanian, Wilson & Cios, 2005; Balasubramanian & Wilson, 2006). For instance, to motivate my middle-school students and sustain their excitement throughout the learning process, I used fake money in all my classes as an incentive to mirror choices and constraints in the real world. This token "microeconomy" in the classroom not only provided students both, individually and collectively, constant and immediate feedback on their performance each class, it also challenged them to become creative problem solvers who were trying to maximize their limited resources. Meanwhile, I embraced the revised two-dimensional Bloom's Taxonomy (Anderson and Krathwohl, 2001) to plan and organize the cognitive elements of my instruction. Using this framework and access to an online communication and assessment tool, SchoolFusion, I could motivate and empower all students by monitoring and managing their work, collect real-time data on their understanding of science and engineering concepts, provide them with immediate feedback, and use this information to guide subsequent instruction (Balasubramanian, 2006). The conceptual model combines these two domains - the cognitive and the affective - that together determine student learning and achievement.

Research Question: Do students gain and retain knowledge better with more feedback and guided inquiry hands-on learning than middle-school students who have less feedback and only direct instruction?

Results

Fig. 1. Summary of Pearson correlations for the four types of knowledge in the filter project

Water Filtration I (18-item Hydrologic Cycle Test)
Treatment: Crossword for vocabulary, No hands-on investigation

Fig. 2. Summary of two-tailed, paired sample t-tests on hydrologic cycle test in the filter project

Water Filtration II (42-item Water Test)
Treatment: Crossword for vocabulary, Followed by hands-on investigation

Fig. 3. Summary of two-tailed, paired sample t-tests on water test in the filter project

Preliminary Findings

Despite the small sample sizes and minimal teacher intervention, the two-tailed, paired sample t-tests show that the mean test scores increased significantly from pretest to post-test for the entire class, even with disaggregated data by gender, ethnicity and services (SPED, ILP, IEP & Math Lab). These gains are statistically significant at the 0.05 level and p <0.0001 means that the there is less than one-hundredth of 1% probability that the observed differences happened by chance. The number after ± in the pretest and posttest mean scores is the error, the standard error of the mean. Besides, the fraction of students' possible gain [= (Posttest-Pretest)/(100-Pretest)] was distinctly greater for the entire class and all four subgroups in the case of hands-on guided inquiry compared to the innovative direct instruction. A limitation of this study using archival data is the lack of control group and random assignment. Nevertheless, for my dissertation study, I look forward to comparing and/or replicating these results in different classroom settings by sharing the curriculum and resources with other science and technology teachers in Colorado to test if the underlying instructional model and conceptual framework are robust.

References

Simon, H. A. (2001). Learning to research about learning. In S. M. Carver & D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress. Mahwah, NJ: Lawrence Erlbaum.

Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman.