learning with insight

Researchers: David Boulanger, Jeremie Seanosky, Colin Pinnell, Jason Bell


LAMBDA is the learning analytics operating system that provides generic and customizable functionalities to sense data, shape the data into experiences, analyze learning experiences, measure the impact of learning activities, and offer reflection/regulation opportunities to improve learning experiences.



LAMBDA Demo 2015.02.08

LAMBDA play button

LAMBDA Full Demo 2014.12.15

LAMBDA play button

LAMBDA is the name of the overall learning analytics system developed by Athabasca University’s research group under Professor Vive Kumar.

Much has been written on big data and learning analytics individually but an important gap lies between both areas. Our research proposes a generic learning analytics competence-based framework called Lambda as a potential candidate to reach big data through learning analytics. Precursor systems to Lambda have already been experimented in programming and in the energy industry and is currently being applied in a Java programming course at Anna University, Chennai, India. For more information about some of Lambda’s tools, please view: Codex, SCALE, MI-DASH, and SCRL. The universal applicability of the Lambda framework to any learning domain as well as its ability to recognize learning artifacts supporting the evidence of higher-level problem solving-oriented competences make it an ideal vehicle to get to big data. Lambda’s researchers and developers are currently working at expanding its volume, velocity, and variety dimensions to consolidate its stance in big data realm.

Learning analytics can be readily exemplified using the analogy of the gold-mining process, where raw gold-laden minerals are extracted, transported to the refinery, and then processed into fine, highly-priced jewels and decorations and/or precious gold bars.

LAMBDA includes three general parts that are implemented in different ways.

Part 1 – Sensing Technologies

We develop client-side technologies that sense learning activities of interest in different areas of learning.

Examples of those client-side sensor-based technologies are CODEX, MI-Writer, ART, SCRL, etc.

These sensors provide the raw data existential to our overall learning analytics endeavor. Basically, those raw data are absolutely meaningless prior to their being processed and analyzed.

The raw learning data can be compared to the raw gold nuggets or gold-laden minerals extracted from different mines. The gold can be extracted in many different ways and may be in many different forms, but without this input of raw gold, no market-ready gold is available.

Those learning sensors are also responsible for transporting the raw data to the refinery (processing engine). They must always ensure a uniform data format that can be handled by the processor.

Part 2 – Processing & Analysis Engine

The processor is responsible for making sense out of the raw data it received from the sensors. It takes each data packet and subjects it to several types of analyses depending on the desired outcome or what we want to understand from it. Then the results of those analyses are stored and made available for use.

Using our gold-mining analogy, different refining processes are used for different types of gold in different forms to achieve different end products. The process used to refine gold into 24-carat jewels is different from the one used in making gold bars used in banks.

Depending on the desired end product, we carefully choose and customize the refining (analysis) process in order to achieve exactly what’s needed.

Part 3 – Visualization & Reporting

The last part of the LAMBDA system is about providing feedback (reporting) to the student and/or teachers about the student’s performance, as assessed by LAMBDA.

The analysis results are meaningful and in their purest form. However, we need a medium by which to convey that valuable information to the students so they can make good use and benefit from the analysis performed on their work.

The most important goal of learning analytics is and should be to analyze how learners are learning in order to provide them with information as to how they are faring in various areas and what are their strengths and weaknesses so they can work on them throughout their learning sessions and improve themselves, thus resulting in better grades and a better knowledge level.

Some students may tend to think they’re bad in all subjects and understand nothing, while others might tend to overestimate their performance. Then when the final examination comes, some may be surprised by their performances while others might be disappointed. That’s where LAMBDA will come into play to tell students the truth about their performance in a real-time environment so they can work on the proper facets as they surface and as a result become much more confident and proficient in the subjects they learn about.

Likewise, after the refining process, gold is in its purest form, but it’s not of much use if it provided as a crude block. It’s now into the hands of the goldsmith to fashion the block of gold into fine jewelry, bank gold bars, electronic components, plating, etc.

In brief, LAMBDA is a learning analytics system still in its debut on which we are working actively in order to make it fully usable and beneficial to learners in general.


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