Data Collection, Processing and Analysis

........refers to the measurement, collection, analysis, and reporting data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.

This is the ..... framework, which use the educational and psychological theory, together with the application of learning analytics to understand deeply learning processes.

Organize these examples of data collecting methods into the right categories

Groups:

(a) Tests, Text data

,

(b) Self-reports, video/audio data

,

(c) Biometric devices

,

(d) Digital traces

• Click patterns

• Body posture

• Learner interactions

• Knowledge

• Heart rate variability

• Transcribed conservations

• Page visits

• Intrinsic motivation

• Intelligence

• Self-efficacy

• Interaction time

• Electrodermal activity

• Eye tracker

• Skills

• Functional near-infrared spectroscopy

• Learner products

• Idle time

Order these data-collecting approaches into increasing degrees of obtrusiveness

• Digital traces

• Video and audio data

• Tests, Self-reports

• Biometric devices

• Text data

What can be the risks of collecting data from a single source or metric? (multiple answers)

• Missing data

• Elaborating signal-to-noise ratio

• Missing contextual information

• Increasing the probability to measure latent aspects

Data......provide the methodological link between collected data and their utilisation in adaptive learning systems

Match the following features to appropriate methods of data processing and analysis

• labeled + unlabelled data

• Discovery with models

• task-driven approach

• Semi-supervised algorithms

• apply validated models

• Unsupervised algorithms

• environment-driven approach

• Supervised algorithm

• data-driven approach

• Reinforcing algorithms

​ Organise these features in correct categories

Groups:

(a) Supervised algorithms

,

(b) Unsupervised algorithms

• unlabelled data

• association

• inputs and coresponding outputs

• clustering

• regression

• classification

Which algorithms should we use for this data set?

• Supervised algorithms

• Unsupervised algorithms

• None of them

• Both of them

Organise these approaches of data processing and analysis into the decreasing degree of interpretability

• Unsupervised algorithms

• Classical statistical method

• Visualization

• Supervised algorithms