#Hyperresearch upload cases software

The LMSA-Kit developed by Hedrick and Graulich is a software solution that captures data on electronic learning from data logs in the LMS. The chapter by Hedtrich and Graulich extends the information presented by Mehta and Kalyvaki and focuses on extending the capabilities of a Learning Management System through a software application LMSA-Kit. Awareness of such important aspects of any program is essential before adding it to one’s research toolkit. An example is the calculation of effect size, which is important in addition to significance test and p-values for statistical analysis. Such default outputs that can be generated by clicking a few buttons in the software interface lack additional tests that are useful. These ouputs may not include a complete analysis sought by the user. The default outputs of various software depends on the sequence of events selected by the user in the software interface. For example, SPSS (Statistical Package for Social Sciences) needs users to provide specific labels for gender or institutions, and other demographic data before doing analysis. A data could have missing values or might need labels. Sometimes software require front-end work by researcher for data organization and data labeling before conducting the analysis. Researchers need to be aware of differences in data handling and analysis procedures among softwares. Recent changes in technology in several forms has made it easier for researchers to gather and organize data in ways that allows systematic analysis and encourages researchers to collaborate across the geographical locations for collective sense making. Irrespective of research methods being qualitative, quantitative or mixed methods, the entire process was complicated, tedious, and time consuming.

For quantitative research, the data had to be manually organized and calculators were used to conduct statistical calculations. The data analysis involved cutting and pasting segments of data and sorting these data segments into bins, and re-organizing (coding qualitative data) until meaningful categories were generated. Post-collection, the data was cleaned and sorted in physical bins for analytic purposes. Tools such as notebook or journals were used to plan a systematic study and various data gathering tools for collecting audio or audio-visual data involved bulky equipment (video cameras and cassette recorders). Every step of research, from planning for research to data collection and analysis had to be conducted manually. Prior to technological advancements conducting research in any discipline was a tedious process. That is where the journey of seeking information or exploring these tools for a specific idea of a chemical education or other discipline based education research projects may begin for our reader, and hopefully culminate into applying one or more tools for digging into the data to make sense of numbers and words

The chapters in this book can be read in any order as the reader may see a connection of any given tool portrayed in a chapter to their own research project. This book is valuable for chemical and science or education researchers alike. The intent is to provide information about availability and application of various computer-based tools of qualitative and quantitative research to diverse researchers, including both novices and experts. It also provides an overview of computer-based data analysis tools (R, jMetrik, Apache Drill, ATLAS.ti, D2L) used in various educational research projects that form various chapters in this book. This chapter is an introduction to computer aided data analysis.

Hyperresearch upload cases