Topic 3 Introduction to analysis of data generated from learners activities , either digital or non-digital

Data mining research

Educational data mining researchers (e.g., Baker 2011; Baker and Yacef 2009) view the following as the goals for their research

1.Predicting students’ future learning behavior by creating student models that incorporate such detailed information as students’ knowledge, motivation, metacognition and attitudes;

2. Discovering or improving domain models that characterize the content to be learned and optimal instructional sequences;
3. Studying the effects of different kinds of pedagogical support that can be provided by learning software; and
4. Advancing scientific knowledge about learning and learners through building computational models that incorporate models of the student, the domain, and the software’s pedagogy.

To accomplish these four goals, educational data mining research uses the five categories of technical methods (Baker 2011) described below.

1. Prediction entails developing a model that can infer a single aspect of the data (predicted variable) from some combination of other aspects of the data (predictor variables). Examples of using prediction include detecting such student behaviors as when they are gaming the system, engaging in off-task behavior, or failing to answer a question correctly despite having a skill
2. Clustering refers to finding data points that naturally group together and can be used to split a full dataset into categories. Examples of clustering applications are grouping students based on their learning difficulties and interaction patterns, such as how and how much they use tools in a learning management system (Amershi and Conati 2009), and grouping users for purposes of recommending actions and resources to similar users
3. Relationship mining involves discovering relationships between variables in a dataset and encoding them as rules for later use. For example, relationship mining can identify the relationships among products purchased in online shopping (Romero and Ventura 2010).
4. Distillation for human judgment is a technique that involves depicting data in a way that enables a human to quickly identify or classify features of the data. This area of educational data mining improves machine-learning models because humans can identify patterns in, or features of, student learning actions, student behaviors, or data involving collaboration among students. This approach overlaps with visual data analytics (described in the third part of this section).

Learning analytics

Technical methods used in learning analytics are varied and draw from those used in educational data mining. Additionally, learning analytics may employ:

• Social network analysis (e.g., analysis of student-to-student and student-to-teacher relationships and interactions to identify disconnected students, influencers, etc.) and
• Social or “attention” metadata to determine what a user is engaged with.

In summary, learning analytics systems apply models to answer such questions as:

• When are students ready to move on to the next topic?
• When are students falling behind in a course?
• When is a student at risk for not completing a course?
• What grade is a student likely to get without intervention?
• What is the best next course for a given student?
• Should a student be referred to a counselor for help?

Visual Data Analytics

Visual interactive principal components analysis (finding the components of a dataset that reduce many variables into few) is a technique once available only to statisticians that is now commonly used to detect trends and data correlations in multidimensional data sets.

Gapminder (http://www.gapminder.org/ ) for example, uses this approach in its analysis of multivariate datasets over time.