Lightning presentation at the Lace SoLAR Flare event

I gave a short presentation about ReflectR during the Lace SoLAR Flare event about “ReflectR – Analysing reflective thinking in texts” on Friday 24 October 2014.

The event took place at the Open University and was under auspices of the Society for Learning Analytics Research (SoLAR) and organized by the LACE project Learning Analytics Community Exchange.

In my presentation I talked about ReflectR, an application of reflection detection. Reflection detection analyses text in order to find evidences of reflection.  In my PhD research I explored several methods in order to understand to which extend it is possible to automatically classify sentences whether they are reflective or not. The aim is to find reflective sentences in a given text, for example in forum posts or essays.

How does it work? You need a large corpus of labelled sentence. This training corpus is then used to built predictive models which can be used to classify unseen sentences. In my case I created a large corpus of reflective and non-reflective/descriptive sentences. Based on this corpus I was able to tune models which perform well on the testing data. These models can be used to classify nascent text.

To make this idea more graspable I built a little tool called ReflectR in one of my free weekends.  ReflectR takes as input a sentence – any sentence written in English. It then processes this sentence, classifies it and returns a value, which is translated into a human readable result. It will tell you if the sentence is reflective and how confident it is with the classification. Try out ReflectR at http://qone.eu/reflectr.

Here is the one page slide for this presentation showing an example sentence, which is automatically processed and classified as reflective. Dewey is greeting.

Lightning presentation ReflectR
Lightning presentation ReflectR

I see ReflectR  as an application of the base technology reflection detection. It is probably not the best application of this base technology, but this is also not its aim. ReflectR was build so that other people can try it  and get an idea what reflection detection is about. I am hoping to find people who are interested in reflective thinking and want to collaborate on innovative applications based on reflection detection.

 

ReflectR – a tool to detect reflective thinking in texts

In my PhD I am researching methods to to detect reflection automatically . Based on a large body of sentences, which were manually labelled as either reflective or descriptive, I tested several text classification algorithms. Each classifier generates a model, which can be used to classify unseen text.

Based on these models I built ReflectR. ReflectR is a tool that you can use to test the classification of sentences. It will analyse your input and will tell you if your sentence is reflective or descriptive/non-reflective. ReflectR will also tell you how confident it is with classifying your text. Have a go and try it yourself at http://qone.eu/reflectr. It is free to use. Once the text is classified please leave feedback. Your feedback is most valuable to my research!

Screenshot of ReflectR
Screenshot of ReflectR

One way to start with ReflectR is to have a look at the examples at the bottom of the page. This will give you a feeling about reflective and descriptive sentences.

If you cannot think of an experience you were deeply were thinking about, start with a descriptive sentence. Write about what happened. This writing exercise might help you to kick-start the reflective thought process. Later you can start writing about yourself. Write in the first person perspective, for example about your beliefs, your feelings, why and how you changed your perspective about something, etc.

ReflectR aims to be a demonstrator for the automated detection of reflection in texts. You could for example use he underlying technology to analyse forum posts of a learning environment. The classification results will give you an overview at which stage of the course people were engaged in reflective thinking.