How can you find the best data for educational technology research?

1 Define your research goals

Before you start looking for data, you need to have a clear idea of what you want to achieve with your research. What is the problem or gap that you want to address? What are the specific questions that you want to answer? What are the expected outcomes or implications of your research? Defining your research goals will help you narrow down your focus and scope, and guide your data selection and analysis.

2 Identify your data needs

Once you have your research goals, you need to identify what kind of data you need to answer your questions. Data can be classified into different types, such as quantitative or qualitative, primary or secondary, and descriptive or inferential. Depending on your research goals, you may need different types of data to provide evidence, context, or insight. For example, if you want to measure the effectiveness of a gamified learning platform, you may need quantitative data on learner engagement, performance, and feedback, as well as qualitative data on learner motivation, satisfaction, and challenges.

3 Explore existing data sources

Before collecting your own data, you may want to explore existing data sources relevant to your research topic. This can save you time and resources while providing valuable information to complement or contrast your own data. Examples of existing data sources for educational technology research include public databases, such as the National Center for Education Statistics (NCES), the World Bank Education Data, and the OECD Education Database. Open educational resources, such as Khan Academy, Coursera, and edX, can provide data on learner demographics, preferences, behaviors, and outcomes. Academic journals, like Computers & Education, Journal of Educational Technology & Society, and British Journal of Educational Technology are publications that disseminate original research findings and reviews on various disciplines. You can use these journals to find data on related studies and learn from their methods, results, and limitations.

4 Collect your own data

If you cannot find existing data sources that meet your data needs, or if you want to generate new data that is specific to your research context and population, you may need to collect your own data. To do this, you need to design and implement a data collection plan that aligns with your research goals, questions, and ethics. When considering the steps of data collection, you must choose the methods that are appropriate for your research design, data type, and sample size, as well as for your participants' characteristics, needs, and consent. Additionally, you should select reliable and valid tools that are user-friendly and compatible with your data analysis and storage platforms. Lastly, it is important to choose procedures that are feasible, ethical, consistent, and compliant with research protocols and regulations.

5 Evaluate your data quality

When you’ve collected your data, you need to evaluate its quality before you move on to data analysis. Data quality refers to how accurate, complete, consistent, and relevant your data is for your research purposes. Poor data quality can lead to incorrect or misleading results, so it’s important to take steps to ensure its accuracy. You can check for errors in your data, such as typos, missing values, outliers, or duplicates, and use various data cleaning techniques like validation rules or filters to correct them. Additionally, you should look for biases in your data due to subjective or objective factors like sampling errors or measurement errors and use various data control techniques like randomization or standardization to minimize them. Finally, you should also check the relevance of your data by identifying and eliminating any irrelevant or redundant data using various data selection techniques like sampling methods or feature extraction.