Evaluation of Research Practices in Academia

Research in Academia

The late 60s were a watershed for academia and research based practices, as new technologies emerged to restructure the painstaking process of collecting, analyzing and managing research data. In spite of initial resistance from faculty, research students began using rudimentary software such as Statistical Product and Service Solutions (SPSS) to undertake sophisticated quantitative analysis. Technological research tools not only alleviated the cumbersome processes of analysis and interpretation, they also lowered the inherent potential for human era. Today, a large majority of higher education institutions have embraced computer assisted qualitative and quantitative analysis for research purposes in different fields including sciences, law, medicine and social studies.

The use of cloud computing has particularly eased the process of data collection and analysis. Only recently, prior to the extensive use of software in academic research circles, undertaking research was a costly and lengthy affair involving questionnaires and surveys. Inevitably, the data collected from the participating subjects was not always accurate and would in effect result in gaps in the analysis of this data. In the absence of assistive technology, experimental research was equally a complex procedure that would require academics and their assistants to manually tabulate and compare numerous scenarios to establish the co-relation between the variable under study.

Perhaps the use of tape recorders in ethnographic studies laid the foundation assistive technology in conducting academic research. Tape recording as opposed to direct note taking was certainly a much easier method of recording and storing data. In a sense, first hand recording allowed the researcher to collect, record and store data without infiltrating the ensuing data with preconceived prejudices, as would be the case when making handwritten notes from observation and interviews.

In the 21st century, the introduction and adoption of computer assisted data analysis software continues to help researches in various steps of the analytic process. Assistive technology aids in the proper management of otherwise complex and cumbersome data that includes notes, numerical codes and text. Indeed, the validity of any research outcome is largely dependent on the careful and consistent management of data. Some technologies assist in generating reports and searching for key terms, while others compile codes and text for conclusive data interpretation.

Lewins and Silver explain that modern software programs undertake more complex functions beyond just searching for keywords and report-generation. Researchers are now using available software programs to assess co-relations between variables in the data collected in an effort to build scientific theory or models. Of course, computer programs alone cannot construct models or theoretical basis in a research project, but these software help researchers to easily study data and thus test hypothesis and generate possible theories. Such programs also support collaborative working amongst researchers, and the development of multimedia data that makes research findings more vivid and tangible for the researcher (p.14).

High definition photography and video technology have surprisingly been incorporated into the academic research process. Researchers are avidly using video and photography technologies for data collection and analysis. It is worthwhile mentioning that the widespread use of internet technology has also changed the methods and settings within which those in academia conduct research. For example, Mendeley, a free and popular social networking platform and reference site for academic researchers lets users find relevant academic papers and read them while on the move, generate bibliographies and collaborate with other researchers.

Lewins and Silver contends that Nvivo8 is equally reliable and versatile computer assisted qualitative analysis software used across many institutions of higher learning. This software allows users to undertake comprehensive data analysis, determine interrelations between different data sets, and establish potential theories through visualization tools. Nvivo8 also supports collaboration through its inbuilt social media network, which integrates YouTube video streaming and photographic technology. This software's interface is modelled on the Microsoft Outlook platform, with the workspace displaying various aspects of the project on a single interface for ease of use. Nvivo8 supports different data formats including multimedia, text, and code, graphic and audio files (23).

ATLAS.ti is powerful qualitative analysis software that enables researchers to store and effectively manage large amounts of data. ATLAS.ti is efficient at enabling the grouping of codes without the need to change the overall scheme of the program. Users are also able to assess the imminent relationship between identifiable variables, and to recover data via transitive relations embodied in the data. Through ATLAS.ti dynamic multimedia platform, researchers can easily link their multimedia quotations to text files, annotate multimedia data, and create multimedia data in different forms including video, images and audio from text-based documents.

Finally, Hyper Research is a valuable academic research tool especially for data collection, analysis and theory building in fields such as law, medicine, social sciences, marketing and political theory. This research technological tool allows for generation and analysis of transcripts in law, multimedia analysis of clinical data, which can be used to make diagnoses, analysis of themes in political discourse and assessment of multimedia data resulting from group interviews and surveys in social studies. As discussed earlier, modern computer assisted analysis software aids the researcher in theory formulation. As a reliable code and retrieve research program, Hyper Research features a theory builder that can categorize related data for testing of hypothesis and formulation of theory ideas (Schutt,p.350).

Tesch argues that although the use of technology in various fields of academia is a laudable milestone in advancing research findings and innovations, there is still much debate about the efficacy of cloud computing software in theory building. Some argues that programs that depend on code and retrieve mechanisms to establish theory models could be biased against other existing and potential theoretical interpretations. However, software developers continue to create advanced programs that are less skewed toward a single analytical methodology, to allow for valid data interpretation and outcomes (p.113-123).

QUANTITATIVE AND QUALITATIVE DATA ANALYSIS AND INTERPRETATION OF INSTRUCTORS' PERFORMANCE IN LINCOLN COMMUNITY COLLEGE

As part of a review of the instructional staff at Lincoln Community College, 70 students undertaking different courses took part in a survey to evaluate the performance of 3 instructors, teaching different courses. The research data is quantified into qualitative and quantitative variables, all of which are applied to assess the overall performance of the surveyed instructors.

The qualitative material compiled and analyzed in the survey comprises of the number of course sections taught in the current term; the number of students enrolled in the first week of the term; the number of students who withdrew from the course; the grade spread as awarded by the instructor; the number of students who filled out the survey ; and the grading scale for instructor's performance ranging from 1-5, with 5 being the highest rating and 1 being the lowest. On the other end of the spectrum is the qualitative data collected which includes students' feedback embodied in the comments they provided concerning the instructor and their overall thoughts about the course.

A quantitative analysis of the available data indicates that Instructor A who initially had 45 students and teaches basic mathematics, faced the highest number of withdrawals, 12, during the course, compared to Instructor B and C. Instructor B, who teaches Computer Basics 101 had up to 120 students enrol in his course but faced the lowest number of withdrawals at 6, while Instructor C who teaches English Literature had the least number of enrolments at 38 students faced an equally low withdrawal rate of 8 students, compared to the overall class size. Even then, Instructor B had the lowest average grade rating of 2.46 out of 5, after surveying the 47 students. Instructor C performed the best, with an average rating of 3.44 out of 5, with Instructor A coming in second with an average rating of 3.34.

A look at the grading scale matrices used to rank instructors' performance indicates that Instructor C recorded the best performance across all indices. The instructor scored 4.7 out of 5 for the evaluation question "teacher knows material" compared to the 4.3 and 2.8 for Instructor A and B respectively. For the evaluation question "teacher helped me understand the material," Instructor C scored 3.5, while A followed closely with a 3.1 score and B with 2.8. Interestingly, Instructor B scored a higher average for the evaluation question "the course materials were helpful" at a score of 3.0, while A and C scored 2.8 and 1.8. This could be an indication of Instructor B's lack of proficiency in classroom-based instructional processes and a demonstration of advanced skills in preparation of relevant course materials.

The grading spread is an indication of the number of students who scored grade A,B,C,D,or F following the end term assessment. There were more students with a grade A, totalling 27, in the English Literature class compared to the Basic Mathematics and Computer 101 course, where were only 5 and 18 students scored grade A respectively. From a qualitative point of view, when these findings are compared to student's grading of their instructors in the evaluation questions, "teacher knows material," and "teacher helped me understand the material," there is a pertinent relationship between the grade spread and the students' perception of their instructor. Instructor C recorded the highest score in both these evaluation questions and had the highest number of students scoring grade A compared to Instructor A and B.

Students' comments and the subsequent grading of the instructors are indicative of the effect of the classroom relationship between students and instructors.

Rotenberg contends that in general, where students perceive that the instructor is involved in helping them understand course material, the students feel that the course was generally successful. The qualitative data indicates that out of the three instructors surveyed, those who allocated more time for assignments performed better than those who were perceived to allocate a limited amount of time for assignments. In particular, Instructor A and C both scored an average of 3.5 marks for the evaluation question, "The time allotted for each assignment was sufficient" while Instructor B scored 1.8. These results could further be an indication of the value the students place on instructor-student class relationships. Students could view shorter assignment durations allocated by the instructor as unrealistic and stressful, leading to the perception that the instructor is unyielding, difficult to relate with and demanding, which can result to antagonistic classroom relations between the instructor and students.

Overall, instructor involvement in simplifying and explaining course material is a determining factor in how students grade their instructors. Given that all three courses were basic introductory courses, students expect instructors to indulge in simple and in-depth explanation of course material instead of assuming that each student already understands the fundamentals at the start of the course. Such a pedagogical approach requires that instructors demonstrate a thorough understanding of course material as well as advanced skills in preparing and presenting course material.

In conclusion, while the qualitative and quantitative data collected from this survey provides a general model for assessing instructors' performance, a significant amount of contextual data is lacking. Importantly, the survey should assess instructors' specific teaching strategies to determine whether the pedagogical methods used are indeed effective or appropriate for the course. The survey would also be positively furthered by the collection of data that shows students' attitudes toward the course and how these affect their performance and as a result, their grading of the instructors.

Rotenberg indicates that such data may answer pertinent questions such as whether students' perception of mathematics and computer science as being difficult subjects could have resulted in their assessment of their instructors as being less conversant with the topic and less helpful in assisting the students understand course material. Attitude research can help to not only evaluate the instructors' efficacy in simplifying the subject matter, but also to assess the student's pre-conceived notions about exact sciences and subsequently, their attitudes toward the instructors who deliver these courses.

References

Lewins, A., & Silver, C.. Using qualitative software: A step-by-step guide. London: SAGE.

Rotenberg, R. L.. The art & craft of college teaching: A guide for new professors & graduate students. Walnut Creek, Calif: Left Coast Press.

Schutt, R. K.. Investigating the social world: The process and practice of research. Thousand Oaks, Calif: Pine Forge Press.

Tesch, R.. Qualitative research: Analysis types and software tools. New York: Falmer Press.