07 Easy Steps for Writing Discussion Section of a Research Paper

The Discussion section represents the analysis of your research paper. It has to be presented in an understandable and intriguing way to the readers who are interested in studying the analysis of results. It is what all of your hard work of writing the hypothesis, collecting and designing the data, conducting the statistical analysis of graphs and preparing the summary sums up to.

The discussion section of a research paper has the sole purpose of explaining every single step from the initiation of the research to the result. Therefore, it is extremely important that a researcher understands how to write the discussion section of a research paper.

Here are the major points to keep in mind while writing the discussion section:

1. Discussion Section should Focus on the Relevance

The purpose of every research is to implement the results for the positive development of the relevant subject. The discussion section should have these major factors listed before beginning to describe how the research was conceived and the sequence of developments that took place.

For example, a Robot navigation system has to handle a large amount of uncertain data in real-life environment. In the proposed experiment Fuzzy logic addresses this problem as it takes uncertain data, processes it and obtains certain and finite data.

2. Discussion Section should Highlight  the Limitations 

If the research is on a subject that might have legal limitations or restrictions that might have caused certain imperfections, it should be acknowledged by the researcher before the work is criticized by others later in his/her discussion section.

Example: Object motion prediction is done by incorporating human experience in the form of fuzzy inference rules. It is assumed that the environment is observed through the stereo vision technique. The observed environment covers the semi-circular area in front of the Robot.

3. Discussion Section should Introduce  New Discoveries

Begin the discussion section by stating all the major findings in the course of the research. The first paragraph should have the findings mentioned, which is expected to be synoptic, naming and briefly describing the analysis of results.

Example: In the proposed work the predictor has been evaluated with three well-known defuzzification techniques. Based on the analysis of results, it has been found that the Mean Of Maximum defuzzification technique has a lower response time and better accuracy

4. Discussion Section should Highlight the Observations

Coming to the major part of the findings, the discussion section should interpret the key observations, the analysis of charts and the analysis of tables. The researcher has to;

           • Begin with explaining the objective of the research, followed by what inspired you as a researcher to study the subject.

           • Explain the meaning of the findings, as every reader might not understand the analysis of graphs and charts as easily as people who are in the same field as you.

           • The reader should be able to understand the key observations without being forced to go through the whole paper.

Case Study:

 Discussion Section:

Figure 1 in the results section represent the average relative error observed for the prediction algorithm for various test cases using Min-Max, MOM and COA defuzzification techniques.

Relative Error Calculation

                        Figure 1: Short-term using Min-Max, MOM and COA defuzzification techniques.

For each test case, the average response time is also calculated to find its suitability to a

real-life environment(Figure 2).

Human capture Shop Centre

                                             Figure 2: Real-life video frames captured at INRIA lab

The prediction algorithm is tested by processing the real-life video frames (which are captured at every interval of 02 seconds). It is observed that the predictor with MOM defuzzification performs better in terms of response time and less relative error. The navigation environment is presented in the form of a prediction graph where the x-axis represents the range parameter and the y-axis represents the Angle parameter. The predicted Angle and Range values are compared with actual values obtained from real-life environments In most cases the predicted values are in the region of the actually measured range and Angle values.

Relative Error

                                        Fig.3. Path Prediction using MoM Defuzzification Techniques

Figure 3. illustrates some of the results obtained for path prediction using MOM defuzzification. The performance of the predictor is tested when more than one object is sensed by the sensor. The response time of the predictor for all the objects should be acceptable for real-life applications. The tests are carried out assuming at most 6-8 objects can be visible and can affect the decisions to be made regarding robot traversal(Figure 4).

                                                    Figure 4:   Comparison of Defuzzification techniques

In real-life applications, the Robot has to deal with multiple numbers of objects and the total response time for all the objects’ motion predictions should be less than the time gap between two sensor readings. The proposed predictor is able to generate results suitable for real-life situations.

5. Compare and Relate with other Research Works

No one is ever the only person researching a particular subject. A researcher always has companions and competitors. The discussion section should have a detailed comparison of the research. It should present the facts that relate the research to studies done on the same subject.

Example: The table below compares some of the well-known prediction techniques with our fuzzy predictor with MOM defuzzification for response time, relative error and Environmental constraints. Based on the results obtained it can be concluded that the Fuzzy predictor with MOM defuzzification has a less relative error and quick response time as compared to other prediction techniques.  The proposed predictor is more flexible, simple to implement and deals with noisy and uncertain data from real-life situations. The relative error of 5-10% is acceptable for our system as the predicted fuzzy region and the fuzzy region of the actual position remain the same.

Table 1: Comparison of well-known Robot Motion prediction Techniques

Short Term Predictor Environmental constraints if any Relative Error Response time in seconds
ANN Predictor A simulated environment with Rectilinear paths 6-17%
Bayesian Occupancy filter Only for small-scale environments Not specified 100 x 10-3
Polynomial NN Simulated environment 1-10% Not specified
Auto Regressive  model Simulated Environment Not specified Computationally intensive
Fuzzy Predictor with MOM Real-life environment 1-10% 07×10-3 sec to 09×10-3 sec

 6. Provide  Alternate View Points

Almost every time, it has been noticed that analysis of charts and graphs shows results that tend to have more than one explanation. The researcher must consider every possible explanation and potential enhancement of the study from alternative viewpoints. It is critically important that this is clearly put out to the readers in the discussion section.

Example: On critical observation of the graphs in environments where the number of objects is less and response time is critical Min-Max method can be used as its response time is better as compared to COA and MOM methods.

7. Show Future Directions

The section must have suggestions for research that should be done to unanswered questions. These should be suggested at the beginning of the discussion section to avoid questions being asked by critics. Emphasizing the importance of following future directions can lead to new research as well.

Example: The authors are in the process of further optimizing the rule base and improving the response time of the predictor. Further improvements in relative error parameter optimization of Min-Max method may lead to a highly efficient MinMax Predictor.

Vijay Rajpurohit
Author: Vijay Rajpurohit

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