Explore practical applications of artificial intelligence in education, from predicting learner performance and dropout risk to analyzing educational content, assessing writing, and identifying labour-market skill needs. This learning path equips educators and learning professionals to interpret AI-driven insights, recognize ethical and privacy considerations, and make informed, data-supported decisions.
By the end of this learning path, learners will be able to evaluate practical AI applications in education, interpret data-driven insights on learner performance, content, assessment, and labour-market skills, and apply ethical, privacy-aware principles to support informed educational decisions.
This module explores the application of artificial intelligence in predicting student performance, providing educators with valuable insights to enhance learning outcomes. The module covers data collection, preprocessing, and the implementation of predictive algorithms, emphasizing ethical considerations and data privacy.
By the end of this module, learners will be able to see how Ai based techniques can predict student performance, analyze educational data to identify patterns, and implement predictive algorithms while considering ethical and data privacy issues.
This module explores the application of artificial intelligence in predicting student dropout rates and success in educational settings.
By the end of the module, learners will be able to understand how AI techniques can predict student dropout or success, analyze data to identify at-risk students, and implement strategies to improve student retention and achievement.
This module looks into the application of artificial intelligence techniques to identify and extract key topics from various educational materials. Participants will explore methods for processing and analyzing text data to uncover central themes and concepts, enhancing the ability to organize and utilize educational content effectively.
Participants will be able to understand how artificial intelligence techniques can identify and extract key topics from educational resources, enhancing their ability to organize and utilize educational content effectively.
This module explores the application of Automated Essay Scoring (AES) systems within educational settings. Participants will gain an understanding of how AES leverages artificial intelligence to evaluate and score written essays, providing timely feedback to students and educators.
By the end of this module, participants will be able to understand and explain the principles and technologies behind Automated Essay Scoring systems, evaluate their effectiveness in educational settings, and identify both the benefits and limitations of using AES for assessing student writing.
In this module, learners will explore how sentiment analysis can be applied to educational materials to enhance learning experiences.
Learners will be able to understand basic sentiment analysis techniques using natural language processing and machine learning algorithms.
This module, provides learners with the foundational skills needed to analyze and interpret educational datasets. Participants will explore various techniques for summarizing and visualizing data, enabling them to uncover patterns and insights that can inform educational strategies and decision-making.
Participants will be able to understand how exploratory data analysis techniques are usually applied on educational datasets, to effectively summarize and visualize data, and derive actionable insights.
This module explores the application of data science techniques to analyze skill needs in the labour market. Participants will learn how to leverage data-driven approaches to identify emerging skill demands and trends, enabling them to make informed decisions in educational planning and workforce development.
Learners will be able to understand how data science techniques can analyze skill needs in the labour market, identify emerging skill demands and trends, and utilize artificial intelligence tools to assess and predict skill requirements.