Sero! Concept Mapping: Free Trial

Mentioned here many times here before, the concept mapping tool Sero!

The Evidence Basis for Concept Mapping: 50 Years and Still Growing

More research-backed reasons to use CM in the classroom

Concept maps' early signs of promise as a learning and assessment tool have helped inspire a 50-year history of evidence building. 

In the U.S., the evidence base proved solid enough for policymakers to recommend concept mapping in the National Assessment of Educational Progress’ 2019 Science Framework.  Indeed, countries as diverse as Costa Rica and India have called for broader inclusion of concept mapping in national-level curricula.

And the evidence continues to grow. Here are some findings from around the world, just since 2020. They illuminate uses for assessment and some of the challenges for implementation that are being effectively addressed by new software innovations.    

1. “Concept maps can be used to replace rote learning with meaningful and enjoyable learning”

The purpose of this study was to find ways to help India’s National Education Policy meet its educational goals with concept mapping. The outcome of this review of the evidence base was highly favorable:

The outcome of the review is that Concept mapping can offer an effective tool in education for both the teaching and learning processs [...] Concept maps provide a unique graphical view of how students organize, connect, and synthesize information [...] which develops critical thinking of the learners. Further, it provides a platform for collaboration and discussion, arriving at shared understandings among members of groups.

Source:

"A journey towards the commitments of national education policy 2020 through concept mapping"

Indian Journal of Science and Technology

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See the benefits of concept mapping for yourself

Sero! is an engaging concept map-based assessment platform. It efficiently harnesses the cognitive benefits of concept mapping to help learners make connections and assessors identify gaps and misconceptions.

Try Sero! for free    

Questions? Email brian@serolearn.com.

Adding Concept Learning to Neural Networks

Linking logic and deep learning,  a great idea.   Did it in the 80s to add analytics and learning to expert systems.   Still not enough support to make it work well.

Deep learning is about to get easier — and more widespread in Venturebeat   By Ben Dickson, Techtalks n @BEBDEE983

(excerpt)

" ... In a paper presented at the ICLR conference in May, researchers from MIT and IBM introduced the “Neuro-Symbolic Concept Learner ", an AI model that brings together rule-based AI and neural networks.

NSCL uses neural networks to extract features from images and compose a structured table of information (called “symbols” in AI jargon). It then uses a classic, rule-based program to answer questions and solve problems based on those symbols.

By combining the learning capabilities of neural nets and the reasoning power of rule-based AI, NSCL can adapt to new settings and problems with much less data. The researchers tested the AI model on CLEVR, a dataset for solving visual question answering (VQA). In VQA, an AI must answer questions about the objects and elements contained in a given picture.

AI models based purely on neural networks usually need a lot of training examples to solve VQA problems with decent accuracy. However, NSCL was able to master CLEVR with a fraction of the data.  ... "