Role of explanatory and exploratory research in this scenario
Researchers across the globe have acknowledged the importance of explanatory and exploratory research in cases where following a particular research approach is usually fruitless. Investigating the cause of failure of a large number of special education students in the new math program is rather complicated. This difficulty in assessing this situation lies in the complexity of the data that need to be collected and inferred from. As they are not simple mathematical entities they can’t be easily measured or used directly for analysis. But this issue can be overcome with the help of the collective use of both explanatory and exploratory research methods. Their importance in this investigation can be explained by analyzing the present situation deeply. But before we can do that, it is imperative that we understand what explanatory and exploratory approaches are.
Exploratory research method is essentially a type of qualitative research approach. A random collection of people (or in this case students or teachers) will be studied, interviewed, or surveyed to finally postulate a probable cause for a particular situation. This conclusion cannot always be accurate since we are not making ample attempt to include all the available statistical data. But explanatory research method primarily uses quantifiable information in order to assess probable outcomes or reasons from or for this data. They are more definite and reliable but elusive and harder to analyze. To be able to make good observations and an accurate assessment this case study needs an integration of both, that is mixed-methods approach . To further clarify this point, consider this situation where, after careful exploratory research, researchers inferred that the failure of these students is due to the inability of their teachers to efficiently explain or use the new math program. They drew this inference from the light of many interviews they conducted with the teachers and the parents of the students. But this conclusion is rather incomplete. It is also possible that the failure of these students was caused by the flaws in the learning tools used by the schools or the difficulty of the students in following this program. The conclusion in this scenario can never be finalized without considering the previous grades of the students and the results (or achievements) obtained by these teachers prior to implementing the new math program. This is where we need quantifiable data for further research.
This shortcoming can be overcome by integrating explanatory methods in research with the existing exploratory methods. The interviews conducted for exploratory research can be quantified and assessed using different mathematical tools . That is, by providing grades to the teachers according to their skills and motivation (such as scoring on a 5 point Likert Scale). And after careful mathematical analysis we could finally understand which factor shows the least performance or which of the factor is the weakest link. Such data could also be used to rate the learning tools used for teaching in each school. And now, after assessing the previous scores, current scores, and the extent to which this new program has succeeded in reaching expectations, we can finally infer, with the least possible error, the real factors that contributed to the failure of these students. This is why we need to integrate explanatory research methods and exploratory research in this investigation.
A Viable Mixed-Method Design to Assess this Situation
Most students attribute their failure or their inability to follow course content due to the lack of care from teachers. This care is a broad term that consists of many factors that influence students positively such as a strong relationship with teachers and their proficiency in instructional approaches . So in this context, the primary factor that should be assessed in this scenario is the teachers involved in conducting and instructing the new math program. Therefore, a quality assessment of their 1. Personal attributes and 2. Preparation done for the new math program is important. Personal interviews or brain storming sessions could be used to make the right conclusion in this front. The third important factor to be considered is the school environment. We should be considering whether the schools in our district were capable of implementing the new math program. This part should investigate whether the schools that conducted the new math program had enough infrastructure, manpower, or experience to effectively implement it.
The committee should select viable candidates from schools (such as students or teachers) for this study. The best practice would be to include teachers with different levels of achievement (whose student performance was 1. Excellent 2. Moderate or 3. Poor) in the new math program. These teachers could be interviewed and surveyed to make a quantifiable data about their performance or find points where they were lacking. The use of RISE (Reading Instruction in Special Education Observation Instrument) is a viable method for observing teachers’ performance . The observant data are then quantified on a 5 point Likert scale where 1 may represent “low quality” and 5, “Best or High quality”. This can prove to be easier while doing mathematical analysis.
But this analysis would be incomplete by just considering teachers in the research process. Other factors that may influence student performances such as 1. Quality of content of coursework 2. Available opportunities for active learning 3. School wide efforts to facilitate learning 4. Collective teacher practices in instructing and 5. School environment, need to be evaluated too for this case .
Data collection for this research can be done by using personal interviews, surveys, and classroom observations. Selected students (the best scorer and poorest scorer) could be separately used for analysis where their previous grades and current grades could be observed together to finally make an assumption for the reason of their failure.
After collecting all the necessary data, careful data analysis should be done to get an answer for this investigation. This is where the experience of the researchers has a definite role to play. The data interpretation cannot simply be statistical. They should be intuitive as well. Researchers have found that, during rating sessions, researchers who approved the use of a specific teaching method tend to rate the teachers who use them highly while the others did not . This is an example of giving into bias. This proves the need of experienced researchers in complicated researches. Only they can integrate intuitive and statistical analysis together and make sound judgments and conclusions based on available data.
References
Bishop, A. G., Brownell, M. T., Klingne, J. K., & Klingner, M. M. (2010). Differences in Beginning Special Education Teachers. In Learning Disabilities Quarterly (pp. 75-92). Council for Learning Disabilities.
Creswell, J. W. (2003). Research Design. London: Sage Publications.
Shaunessy, E., & McHatton, P. A. (2008). Urban Students’ Perceptions of Teachers. Florida: Springer Science+Business Media.
