The primary objective of this project is to utilize AI and machine learning to improve the analysis of mixed-methods research involving surveys and focus group designs for program evaluation. By leveraging advanced technologies, we seek to gain a more comprehensive understanding of the data, extract meaningful insights, and enhance the efficiency of analysis processes.
Total project length: 175 hours
Sentiment Analysis: Apply natural language processing techniques to analyze the qualitative data collected from focus groups. By using sentiment analysis, we can identify the sentiment and emotions expressed in the responses. This can provide insights into the participants’ attitudes, perceptions, and experiences related to the evaluation outcomes being assessed.
Topic Modeling: Utilize machine learning algorithms for topic modeling to identify key themes and topics from the qualitative data gathered in focus groups. This can help categorize and understand the different perspectives and experiences shared by participants. By analyzing and extracting meaningful topics, you can gain a deeper understanding of the evaluation outcomes and identify common threads across different groups.
Text Classification: Implement text classification algorithms to categorize and analyze the survey responses collected in quantitative research. This can help identify patterns, trends, and correlations between different outcomes and specific survey questions. By automating the categorization of responses, time and resources in analyzing large datasets will be saved.
Predictive Modeling: Use machine learning algorithms to develop predictive models that can forecast outcomes based on survey responses and qualitative data. By training these models on historical data, we can identify predictive factors and variables that influence evaluation outcomes. This can help inform future program planning and intervention strategies.
Data Integration: Combine survey data and qualitative data using machine learning techniques for a comprehensive analysis. By integrating both quantitative and qualitative data, a more holistic understanding of the evaluation outcomes being assessed will be gained. Machine learning algorithms can help identify relationships, patterns, and insights that may not be apparent when analyzing each data type independently.
Data Visualization: Utilize AI-powered data visualization tools to present the findings from the mixed methods research in a clear and impactful way. By visualizing the survey responses, qualitative data, and analysis results, effective communication of the research findings to stakeholders is made possible.
Proficiency in programming languages such as Python, Java, or C++, knowledge of machine learning frameworks, solid understanding of statistical analysis, data visualization, and data preprocessing
Easy to Intermediate
Please use this link to access the test for this project.
Please DO NOT contact mentors directly by email. Instead, please email human-ai@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.