# The Efficacy of MOOC to Support Students in Pedagogical Research
## 3 Materials and Methods  
### Mooc--Experimental Data Processing  
We created the course in the Open edX environment in a virtual server supported by Bitnami. The most visible part of Open edX is the LMS. Through it, students can access to educational content and it also provides the necessary management tools to users with administrator, instructor or teacher privileges. LMS uses several data repositories or databases. The educational content, texts, images, and other materials are stored in the MongoDB database, videos are displayed on YouTube or Amazon S3. The data of course participants and their activities are stored in a MySQL database. The Open edX environment (Figure 1) is fully supported for mobile environments, the support for iOS and Android allows access to complete educational content. The created course Experimental data processing was placed on the website of the Department of informatics UKF in Nitra, Slovakia. The course aimed to clarify the particular phases of the statistical processing of the experiment. The phases of the experiment:  
1. Creating of control and experimental group. 2. Creating of reliable and valid measuring procedures. 3. Realization of the experimental plan. 4. Understanding the data. 5. Verifying the validity of the used statistical methods. 6. Data analysis and interpreting the results. The course is designed as a guide to the various stages of processing the experiment. Gradually, the participant gets become acquainted with the procedure of processing the experiment, from the creating of experimental and control groups to the actual processing and interpretation of the results. It introduces various methods of verifying the reliability and validity of didactic tests and also it offers the user several experimental plans for the experiment. It emphasizes the understanding of data, which means to focus on the calculation of descriptive characteristics and confidence intervals and based on these results to build a null statistical hypothesis. From the methods for verifying the defined hypothesis, we chose the two most commonly used methods, the analysis of variance and the analysis of covariance. The course also deals with the assumptions of using both methods. The main parts of the course form the particular phases of the experiment. The structure of the course:  
Introduction  
1. Creating of control and experimental group  
1.1 Random selection  
1.2 Example  
2. Creating of reliable and valid didactic tests  
2.1 Calculation of reliability  
2.2 Calculation of concurrent validity  
2.3 Overview of estimates to assess the quality of the measurement  
2.4 Example  
2.5 Analysis of the reliability of the electronic didactic test: Case study  
2.6 Calculation algorithm  
3. Experiment  
3.1 Realization of the experimental plan  
3.2 Example  
4. Understanding the data  
4.1 Descriptive statistics  
4.2 Confidence interval  
4.3 Visualization of descriptive characteristics  
4.4 Example  
5. Verification of the validity of the statistical methods used  
5.1 Assumptions for using the analysis of variance  
5.2 Assumptions for using the analysis of covariance  
Figure 1: edX Studio environment. 5.3 Example
6. Data analysis and interpreting the results
6.1 The analysis of variance and the analysis of covariance
6.2 Example
6.3 Evaluation of user interfaces for creating database requests: A case study
6.4 Calculation algorithm  
Summary  
The course is designed as an aid in processing the experiment, it has the character of an electronic teaching aid, which means students are expected to work independently with this material. The user does not have to memorize the course content, they only need to be able to work with this material and use it as an aid in the statistical processing of the experiment. Almost every part is supplemented with visual material such as pictures, graphs, tables, and animations for a better understanding of the issue.