Technology application and student/learners experience

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Determination of data to be collected

Qualitative data and quantitative data

In order to investigate the use of the Webinar Technology in education and critically explore the value that the use of such technology adds to learning and teaching experiences; the both qualitative and quantitative data will be collected to focus on both the student’s and the teacher’s perspectives.  The qualitative data will provide a rich and detailed picture of the value that Webinar technology will add to both teaching and learning experience from the student and the teacher’s perspective.  Onwuegbuzie and Leech (2005: p. 375) suggested that the qualitative data contributes to the exploration of the teachers and students experiences and change in behavior or feeling and attitude towards the technology integrated learning and teaching process. Additionally, the qualitative data will describe the characteristics of technology in shaping changing the experiences of both teachers and students in the teaching-learning context.  Qualitative data fosters the investigation of the students’ and the teachers’ attitudes, beliefs and preferences that build up to shape the new experience, provide an opportunity for systematic, in-depth evaluation of the research question that can be answered through the quantitative data. Further, adding value to the quantitative results through explanation and clarification with the target population.

The quantitative data fosters the exploration of the technology usage in learning and teaching and the number of student and teachers that experience positive or negative experience. Since the students and teachers are sampled to participant in the research, the quantitative data will describe and provide a snapshot of the sample and user populations. Creswell (2013) suggested that the integration of the quantitative data resonates to more reliability and validity of findings leading to a valid conclusion on the how technology has changed the learning and teaching environment.  Therefore, the quantitative data enhance and support the understanding of the qualitative data that leads to the development of a comprehensive understanding of learners and teacher experience.

Determination and justification of research approach for data collection

Pragmatic Approach (Mixed Approach)

The research will use the pragmatic approach that will render the researchers the freedom to implement and apply any of the methods, techniques, and procedures that suit the investigation of the use of webinar technology in learning and teaching. Johnson and Onwuegbuzie (2004) explained that the pragmatic approach to data collection will integrate the data collection techniques and methods that best fits the situations. For instance, in this case, the sampled participants will be interviewed to explore the relationship between the application of technology in learning and teaching and the participants’ experiences. The utilization of the pragmatic approach to data collection enhances the transformation of the qualitative data into quantitative data as well as quantitative data to qualitative data to explore all the tenets and facets of the technology application in education.  The pragmatic approach integrates the qualitative involving the interviews to obtain the information that will contribute to the development of the framework to explore the learners and teachers experiences as a result of the implementation and integration of technology in teaching and learning while comparing the traditional approaches and the blended learning.

Determination and justification of data collection method

Interviews as data collection method

The research will utilize the interview methods, especially when gathering data from the sampled student groups. The interviews will contain both the structures, semi-structure and non-structure question. The students will provide their perspective regarding the implementation and expound on changes that have occurred due to the integration of technology. The interview method of data collection increase accuracy and encourages the answer falsification that will contribute to more reliable findings. The interview method provide the opportunity for the capturing of the verbal and non-verbal questions that include body language to gauge the enthusiasm experienced by the students and teachers through the use of technology in education.  Therefore, the interview method will explore the students’ and teachers’ views, experiences, beliefs, and motivation while fostering the generation of the qualitative data. The sampled student group will generate information on the collective views and the meanings behind the views to enhance understanding of the experience and beliefs concerning technology application in education.

Questionnaires

The research will utilize the questionnaires to establish and develop the teachers’ perspective on the implementation and application of technology in learning and teaching. The questionnaires will consist of a series of questions that will facilitate the gathering of information from both the teachers and students.  Questionnaires will suit in this research context since it fosters the collection of information from a large number of people within the shortest period leading to reliability and validity as well as maintaining the highest objectivity to investigate technology application and its effects on the student or teachers experience. Researchers suggest that the questionnaires allow for the quantification of data that allows for comparison and contrast other research to measure change (Johnson and Turner 2003: p. 297) and new experiences, beliefs, behaviors and attitudes among learners and teachers. Thus, enhance uniformity, greater validity, ensure anonymity and rapidity.

Determination and justification of data analysis method

Statistical data analysis (measures of central tendency) and Regression methods

The research will utilize the statistical data analysis and regression methods to analyze the data. Statistically, the measure of central tendency will describe the set of data by identifying the central position within the set of data (Ramsay and Silverman, 2002). The research will utilize the arithmetic- sample mean (average) to determine the trends of data collected, explore the number of observation on the sample and provide a snapshot of the application of technology and teachers/learners experience in education. Further, the research will use standard deviation to explore how the data is spread, especially the determining the number of participants that have shown positive experiences aligned with the application of technology.  The standard deviation will determine the dispersion of data points to understanding the relationship between technology application and learners/teachers experience. Finally, the research will utilize the regression models to explain the relationship between the dependent variable (learners/teachers experience) and application of technology.  The regression will use the data to identify the relationships among the variables and utilize the relationships to make predictions. Therefore, regression analysis will help to understand how the learners/teachers experience will change with the application of technology and increase the value that technology brings to the teaching and learning process.

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  1. Creswell, J.W., 2013. Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
  2. Johnson, B. and Turner, L.A., 2003. Data collection strategies in mixed methods research. Handbook of mixed methods in social and behavioral research, pp.297-319.
  3. Johnson, R.B. and Onwuegbuzie, A.J., 2004. Mixed methods research: A research paradigm whose time has come. Educational researcher, 33(7), pp.14-26.
  4. Onwuegbuzie, A.J. and Leech, N.L., 2005. On becoming a pragmatic researcher: The importance of combining quantitative and qualitative research methodologies. International journal of social research methodology, 8(5), pp.375-387.
  5. Ramsay, J.O. and Silverman, B.W., 2002. Applied functional data analysis: methods and case studies (Vol. 77). New York: Springer.
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