Learning Analytics System for Step Based Learning Activities


Infocomm - Big Data, Data Analytics, Data Mining & Data Visualisation
Infocomm - Educational Technology


Learning analytics systems collect, measure and report data about learners to support instructors in understanding students’ learning.

These learner profile insights can assist instructors in making informed interventions to optimise learning.

Existing technologies mainly make use of log data collected from the learning environment and aggregate statistics such as number (no.) of access to learning resources, learning activity attempts and performances. Those statistics will be used as data features associated with students’ learning patterns to train predictive models.

While the features related to resource accesses and performances are insightful, the data that captures the process of how students solve problems in learning activities could provide teachers a broader overview on students’ mastery of the knowledge and problem-solving strategies.

For this learning analytics system designed to support analysing step-based learning activities, the technology is intended to collect, analyse and visualise students’ problem-solving steps and patterns.

By correlating the analysis result with resource access and performance, teachers, as well as students themselves, can have a more informed intervention/reaction on how to improve learning and achieve learning outcome.


  • The database persists logs of students’ actions on the online learning platform; the raw log data will be processed by the data processing pipeline to extract features such as problem-solving steps, patterns, strategies as well as statistics on resource access and performances.
  • The analysed result will be stored and feed to visualisation system for teachers and students to analyse potential learning patterns and pre-configured insights.
  • Another feature of note is that the raw log data is first to be processed as learning actions and other learning data and stored in the temporal model; afterwards, learning algorithms and aggregations will be carried out based on the processing pipeline to enrich the temporal model and build up summary model.


The technology could also be used for analytics and business intelligence on sequence-based tasks and processes.

Through the adoption of such a technology, features in the sequences could be extracted, analysed and presented.

Market Trends & Opportunities

Currently, for setting up analytics functions, companies could either make use of basic data processing and visualisation libraries to build the entire analytics system from scratch, enabling the total customisation of the tools; or, making use of existing systems such as Tableau and QlikView to realise the required functions on the system, lacking of the ability for free customisation.

This technology is designed to provide a solution in between the two approaches, if the data science problem the company is looking to resolve and analyse for, is a sequential learning or workflow process.


The technology provides a generic analytics framework. Through the adoption of such framework, customers could setup analytics and business intelligence functions easily on top of their existing technology infrastructure.

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