The argument of implementing technology into the educational environment has both followers and opponents (Murthy, 2013). The importance of this issue is being discussed due to its significance to the quality of education (Kurt, 2012). Existing research demonstrates both positive and negative connotations to the use of technology in teaching space. The positive effects include the increased enthusiastic perception of the lesson and greater involvement (Shapley, Sheehan, Maloney, & Caranikas-Walker, 2011).
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Conversely, the negative effects contain an improved level of unresponsiveness and probability to get distracted (Carroll, 2011). The question to be answered in this study is whether the implementation of technology in classrooms is an efficient practice that can generate the desired positive effects on learning.
The statistical analysis will be conducted using the data from the surveys of teachers and principals at public middle schools, as well as reports on student performance in the schools.
The study will analyze the performance of the students from two identical grades from two different schools of the same educational level so that the error percentage would not exceed the permitted limit (Karvounidis, Chimos, Bersimis, & Douligeris, 2014). A total of 54 participants (2 principals, 2 teachers, 50 students) will represent the relevant population. The maximum variation sampling strategy is chosen to maximize the variety of the outcomes pertinent to the research question. The demographic that the participants share is the location as they attend the school in the same area. Other demographics, such as gender or age of the students, are nominal to the present research question and study.
The questionnaires will be distributed via e-mail after the permission has been obtained. On the basis of these questionnaires, the research will be able to evaluate the results of the paired sample T-test. The purpose of the T-test is to reflect on the changes in the students’ performance, which is the dependent variable, measured with ratio scale (So, 2012). The nominal independent variable will be the use of IT and will be measured according to the yes/no scale.
The paired sample T-test will provide insight into comparing the results from two schools, where one of the schools will eventually abandon the use of IT in the education. The research will reach the verdict on whether the technology has any positive effect on the education among the K-12 sample and to what extent (Smetana & Bell, 2012). The research uses the paired sample T-test strategy because the statistical implication will designate whether the alteration between sample medians is expected to epitomize an authentic difference between the two groups, and the resulting size will signpost whether that variance is sufficiently large to be practically convincing (Edyburn, 2013).
The research is targeted to gauge the level of the impact of technology on student assessment and is expected to dwell on the relationship between the IT involvement into the learning process and consequent students’ grades (Barak, 2013). The paired sample T-test is projected to display the variance between the indices of the control and experimental student groups. The research is expected to display the improved performance in the classes where teachers use the IT.
The research has limitations in the present situation, which are the possible lack of resources and computer illiteracy of the teachers. The further research should reflect the visual dependence of the criterion on the use of technology in K-12 education. The major asset of the present research is that the T-test will indicate the students’ performance progression. The paired sample T-test is the core of the proposed quantitative research, which is designed to establish if the use of IT will improve students’ performance. If the hypothesis is proved, the results will primarily concern teachers and principals as they will present them some facts to consider concerning the introduction of technology in education.
Barak, M. (2013). Teaching Engineering and Technology: Cognitive, Knowledge and Problem-solving Taxonomies. Journal of Engineering, Design and Technology, 11(3), 316-333. Web.
Carroll, J. (2011). From Encyclopaedias to Search Engines: Technological Change and Its Impact on Literacy Learning. Literacy Learning: The Middle Years, 19(2), 27-34.
Edyburn, D. (2013). Critical Issues in Advancing the Special Education Technology Evidence Base. Exceptional Children, 80(1), 7-24.
Karvounidis, T., Chimos, K., Bersimis, S., & Douligeris, C. (2014). Evaluating Web 2.0 Technologies in Higher Education Using Students’ Perceptions and Performance. Journal of Computer Assisted Learning, 30(6), 577-596. Web.
Kurt, S. (2012). How Do Teachers Prioritize the Adoption of Technology in the Classroom? Teachers and Teaching, 18(2), 217-231. Web.
Murthy, K. (2013). Fun Based Education for Ages from 3 to 100: Lighting the Candle of Learning Passion. 2013 3rd Interdisciplinary Engineering Design Education Conference, 10-18. Web.
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Shapley, K., Sheehan, D., Maloney, C., & Caranikas-Walker, F. (2011). Effects of Technology Immersion on Middle School Students’ Learning Opportunities and Achievement. The Journal of Educational Research, 104(5), 299-315. Web.
Smetana, L., & Bell, R. L. (2012). Computer Simulations to Support Science Instruction and Learning: A Critical Review of the Literature. International Journal of Science Education, 34(9), 1337-1370. Web.
So, W. (2012). Creating a Framework of a Resource-based E-learning Environment for Science Learning in Primary Classrooms. Technology, Pedagogy and Education, 21(3), 317-335. Web.