This research seeks to establish the level at which the K-12 online teaching model is effective. The K-12 online program is a new model of learning that emanates from the ongoing efforts to embrace the adoption of information and communication technology in customizing learning. It is argued that this model of learning increases the administrative capacities of teachers and education administrators.
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Being a highly embraced model of learning, it is presumed that a lot of challenges emanating from the continued efforts to put this model into practice. It is important to establish the strengths and the possible risks that come with the K-12 online support programs in order to develop recommendations that are critical in streamlining innovation in learning using this model.
Review of literature
The availability and increased development of information and communication technologies have resulted in the need for deploying technology in the learning environment. One of the modalities on which technology can be deployed in educational institutions in the development of efficient learning models through the utilization of technology (Fillion et al., 2009). The K-12 online program reflects the efforts of utilizing technology in improving the administration of education in the United States and other countries where the K-12 education system is used.
The interest of a substantial number of researchers is to establish the essence of deploying technology in the education system and the payoffs it brings in the administration of education and the impacts on the students who are the end targets of such initiatives. The concern of the researchers comes from the fact that online curriculums still face a number of impediments when applied in higher institutions of learning. This raises concerns over its applicability in the K-12 education curriculum. The validity and efficiency of the K-12 online curriculum for education administrators in the United States differ from the way the higher learning online curriculum is established, yet the expectations are similar; increasing the efficiency and effectiveness of educational administration (Hung & Zhang, 2008).
Jui-Long, Yu-Chang, and Rice (2012) ascertained the need to incorporate data mining in order to develop deeper insights as part of increasing the content on which decisions are made. According to these researchers, the data mining techniques that have been deployed in institutions of higher learning can be used in initiating evaluation models for the K-12 online education programs. This can be likened to the research that was conducted by Hung and Crooks (2009), which reiterated the essence of using data mining techniques to evaluate online learning programs on the side of the students, as well as the side of teaching or education administration.
The evaluation of online learning enhancement programs such as the K-12 online program can only be best done by digging into the benefits that accrue from the learning on the students, as well as the way the program enhances the capacity of educational administrators to deliver. In their assessment of the contribution of the online curriculum in increasing the quality of educational administrators, Hung and Zhang (2008) found out that greater variations still prevail in the behavior and patterns of online learning. This results in varied outcomes, which is an area that attracts more research.
According to Fillion et al. (2009), the characteristic of the students in the K-12 education system is a critical factor in the determination of the professional quality of teachers and other educational administrators. Barber and Mourshed (2009) observed that availing and continued technical support is critical to the sustainability of an online curriculum for the teachers in the K-12 online education curriculum.
Technical support entails the provision of adequate physical materials that are required to administer the curriculum, as well as the provision of the technical information required. The implementation of an online curriculum is eased, especially when it is applied to the K-12 education system in the United States (Barber & Mourshed, 2009). The United States has a highly supportive information technology platform on which online programs can be established and sustained. The teaching and learning processes have to be aligned with the learning process in the K-12 education system to enhance continuity.
This continuity can only be attained when the platform for information technology adoption for the education administrators is properly grounded. The content of the training programs is, therefore, of greater essence as far as the determination of the quality of education administrators is concerned. According to Jui-Long, Yu-Chang, and Rice (2012), most of the failures of the online programs for K-12 administrators result from the failure to consider the pre-training programs. Such programs are critical in guaranteeing the learners the ability to easily adapt to the learning programs.
In their research about the modalities of evaluating the online curriculum for educational administrators, Jui-Long, Yu-Chang, and Rice (2012) observed that it is important to make a comparison of the individual learning programs in order to detect the differences from which the weaknesses can be identified in relative terms.
This is a comprehensive research that seeks to investigate the efficiency and effectiveness of online curriculum for educational administrators in the K-12 education system in the United States. Both quantitative and qualitative research approaches will be deployed in the research. Comparison of the diverse online teaching programs will be used in order to ascertain the differences in the levels of efficiency that are attained under each curriculum. In this way, it will be easy to determine the differences in the modalities of learning in the programs and the ability of the programs to equip the administrators.
The quantitative research method is critical in this research. This method will be used to enlist the specific online curriculum and the selection of the research population from among each set of population. This research method is important due to the fact that this is primary research bases on real cases. The qualitative research methods will be used to get information from the various samples on given aspects of training and professional development under the online curriculum. This research method is important as far as the collection of backup data is concerned. The backup data aids in the analysis and justification of the facts and figures that will be collected using the quantitative research method; that is, quantitative data.
Data collection methods and tools
The main population in this study is the K-12 learning institutions that embrace the online training programs for their tutors. The K-12 schools that do not embrace the online training programs will also be part of the population in order to attain the comparative aspect. Theoretically, the research population will be divided into two for ease of sampling. The main study samples will be obtained from each of the two sets of the main population using a simple random sampling method.
This is aimed at reducing the level of biasness in this research. In order to ease the work on the side of the researchers and make them focus more on the objective of the research, the simple random exercise will be repeated to attain the main sample of ten institutions, five under each set of the population as has been earlier explained. For each institution, simple random sampling techniques will also be applied in order to narrow down to specific sample populations, for example, the sample population from which the qualitative information will be sourced for example the experience of individual education administrators about the online training curriculum.
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Different data collection methods will be deployed because of the comparative aspect of the research, as well as the need to explore the positive and negative aspects of online training for administrators in the K-12 curriculum. These include observation and interviewing, as well as recording for the purposes of further synthesis and analysis of data. The data collection tools that will be used in this research include questionnaires, interview guides, and recording equipment.
Quantitative data will be targeted first in a set of final samples in order to attain a higher level of objectivity in this research. This means that observation and recording will be given the first priority. The rationale behind this organization in data collection is that the quantitative data forms the basis of information that is required. This data is the basis on which a comparative analysis will be done. This will be followed by the collection of qualitative information, which is also relatively important in the research. This means that the questionnaires and the interview guides will be deployed as secondary mechanisms of collecting data from the respondents.
As observed earlier, the nature of the research attracts the deployment of the random sampling technique in the selection of the final sample and the backup of the quantitative data with the qualitative data in order to significantly bring down the levels of biasness. Graphs and tables will be developed in order to simplify the synthesis of the data collected. The development of the graphs and the tables will be based on the comparative aspects of the qualitative and quantitative data. It is important to note that the main deductions in this research will be arrived at after a comparative analysis of the data that will be presented under the two research categories enshrined in the main research.
Barber, M., & Mourshed, M. (2009). How good education systems can become great in the decade ahead. Report on the International Educational Roundtable: Singapore.
Fillion, G., Limayem, M., Laferrière, T., & Mantha, R. (2009). Integrating information and communication technologies into higher education: investigating onsite and online students’ points of view. Open Learning, 24(3), 223-240.
Hung, J. L., & Crooks, S. (2009). Examining online learning patterns with Data Mining techniques in peer-moderated and teacher-moderated course. Journal of Educational Computing Research, 40(2), 183–210.
Hung, J. L., & Zhang, K. (2008). Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching. MERLOT Journal of Online Learning and Teaching, 4(4), 426–437.
Jui-Long, H., Yu-Chang, H., & Rice, K. (2012). Integrating data mining in program valuation of K-12 online education. Journal of Educational Technology & Society, 15(3), 27-41.