Promoting reflective thinking is an important educational goal. A common educational practice is to provide opportunities for learners to express their reflective thoughts in writing. The analysis of such text with regard to reflection is mainly a manual task that employs the principles of content analysis. Considering the amount of text produced by online learning systems, tools that automatically analyse text with regard to reflection would greatly benefit research and practice. Previous research has explored the potential of dictionary-based approaches that automatically map keywords to categories associated with reflection. Other automated methods use manually constructed rules to gauge insight from text. Machine learning has shown potential for classifying text with regard to reflection-related constructs. However, not much is known of whether machine learning can be used to reliably analyse text with regard to the categories of reflective writing models. This thesis investigates the reliability of machine learning algorithms to detect reflective thinking in text. In particular, it studies whether text segments from student writings can be analysed automatically to detect the presence (or absence) of reflective writing model categories. A synthesis of the models of reflective writing is performed to determine the categories frequently used to analyse reflective writing. For each of these categories, several machine learning algorithms are evaluated with regard to their ability to reliably detect reflective writing categories. The evaluation finds that many of the categories can be predicted reliably. The automated method, however, does not achieve the same level of reliability as humans do.
|Creators||Ullmann, Thomas Daniel|
|Source Sets||Ethos UK|
|Type||Electronic Thesis or Dissertation|
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