Physical Education Research

Author: Zhu Tiecheng
Publisher:
Publish Date: 2002-07-01
Features: in the excerpt represents X squared.
I. The Concept of Qualitative Analysis
Qualitative Analysis in physical education research refers to the process of applying scientific thinking methods to collected, organized, non-quantitative data, distinguishing truth from falsehood, and gaining an in-depth understanding of the rationality of physical education phenomena. Proper use of qualitative analysis should pay attention to the following:
1. Authenticity of Data
Qualitative analysis relies on a large amount of non-quantitative data. These materials should be genuine, representative, and substantive. It is important to recognize that the collection and description of such data may be biased or subjective, and a critical approach should be taken to evaluate their authenticity and reliability. If the analyzed data is false, how can the correctness of qualitative analysis be guaranteed?
2. Rationality of Analysis
The main methods of thinking include analysis, synthesis, abstraction, generalization, and reasoning. Correct thinking must adhere to scientific principles and repeatedly examine and verify the conclusions drawn. Otherwise, if errors occur in thinking, the conclusions of qualitative analysis will naturally be fallacious. For example, analyzing the following material: students find physics difficult to learn, and some underperforming students are even intimidated by it. In class, they often "find it boring, look for opportunities to rest, first daydream, then fall asleep," engaging in a disinterested, knowledge-seeking attitude that leads to declining grades. If the analysis of this factual material concludes that students' poor performance in physics is due to their weak foundation or sheer stupidity, such a conclusion is logically untenable. The reasons students fear and lack interest in physics may be multifaceted: it could be due to improper teaching methods by the instructor, the textbook not aligning with students' cognitive rules and psychological levels, or inherent factors in the students themselves. Qualitative analysis is widely used in physical education research. Researchers use it to form rational understandings of educational phenomena. The data for qualitative analysis is non-quantitative, making it prone to bias, overgeneralization, or fallacy. Therefore, researchers should strive to collect comprehensive data and scientifically analyze it, discarding non-essential and minor factors to uncover the essence of educational phenomena.
II. Application of Qualitative Analysis in Physical Education Research
Qualitative analysis is primarily applied in the following aspects in physical education research:
1. Inferring the Nature of the Problem
Through qualitative analysis, the nature of the research problem can be inferred. For example, Teacher Zhang Xianling has explored the discovery-based teaching method centered on experiments for several years. In teaching topics such as simple pendulums, isothermal changes of gases—Boyle's Law, Ohm's Law, resistance measurement, Joule's Law, Faraday's Law, and lens imaging, he conducted planned and purposeful experiments centered on experiments. After multiple trials, he collected a large amount of feedback information and experiential materials. Teacher Zhang concluded that the discovery-based teaching method centered on experiments has the following advantages:
(1) The teaching format is lively, stimulating students' interest in learning.
(2) It encourages active thinking and initiative from students.
(3) It deepens students' understanding of textbook knowledge.
(4) It helps cultivate students' abilities and develop their intelligence.
These conclusions are derived from the qualitative analysis of perceptual materials in educational experiments, demonstrating the characteristics of discovery-based teaching centered on experiments.
2. Enumerating Facts to Support Arguments
Fact materials are enumerated to prove arguments. For example, Teacher Guo Liying conducted experiments and theoretical explorations on "successful teaching" in junior high school physics. In the experimental summary, Teacher Guo not only used quantitative analysis (students' grades) but also provided several factual arguments to demonstrate the effectiveness of "successful teaching":
(1) Students in the experimental classes of ten schools generally outperformed those in regular classes, with superior learning emotions, interests, and will.
(2) Among the four first-prize winners in the 1989 provincial junior high school physics competition, two were from experimental classes.
(3) In the final exams of three schools, the experimental classes achieved a 100% pass rate and a 50%–60% excellence rate.
These facts effectively substantiate the effectiveness of "successful teaching," providing vivid and convincing evidence.
3. Combining Qualitative and Quantitative Analysis to Support Arguments
Quantitative analysis in educational research provides reliability based on "quantity," often more persuasive than citing a few typical examples. However, it does not exclude qualitative analysis. With the support of qualitative analysis, "quantity" gains greater persuasiveness when contrasted with "facts." For example, the Wenzhou High School Physics Reform Experimental Group explored and experimented with "inquiry-based unit structure teaching" with six steps: "stimulating interest and guiding—self-directed learning and questioning—distinguishing doubts and concise explanation—problem-solving analysis—summary and testing—post-learning compensation." After experimental comparisons, the average score differences between the experimental and control classes are shown in Table 2-6-1.
Table 2-6-1 Experimental Comparison of Inquiry-Based Unit Structure Teaching
┏━━━━━┳━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ ┃ ┃Experimental Before┃Experimental After┃
┃ ┃ ┣━━━━━┳━━━━━╋━━━━━┳━━━━━┫
┃Group┃ ┃ ┃ ┃ ┃ ┃
┃Experimental Class┃56┃55.2┃13.8┃64.3┃10.6┃
┣━━━━━╋━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━┫
┃Control Class┃56┃52.3┃12.1┃57.1┃11.7┃
┣━━━━━╋━━━━╋━━━━━┻━━━━━╋━━━━━┻━━━━━┫
┃Mean Difference┃ ┃2.9┃7.2┃ ┃ ┃
┣━━━━━╋━━━━╋━━━━━━━━━━━╋━━━━━━━━━━━┫
┃Statistical Z┃ ┃1.18┃3.42┃ ┃ ┃
┣━━━━━╋━━━━╋━━━━━━━━━━━╋━━━━━━━━━━━┫
┃P Value┃ ┃>0.05┃ ┃ ┃ ┃
┗━━━━━┻━━━━┻━━━━━━━━━━━┻━━━━━━━━━━━┛
Note: X and Y represent the mean scores before and after the experiment, while Sx and Sy represent the standard deviations of the two sets of scores.