Use of a bilingual text-mining system for learning and teaching and assessment

Hong Kong education sectors emphasize assessment. The curriculum delivery in local schools tends to be examination-oriented; and the assessment focus tends to be students’ examination results. Education in the 21st century is envisioned to prepare learners for domain knowledge development through an active, constructive and interactive learning process in technology-enhanced environments. This educational goal creates the opportunity for teaching practitioners to put more effort on the learning and teaching process and formative assessment, in which teachers continuously know learners’ progression and provide timely learning feedback for learners. This talk introduces the development and implementation of a bilingual text-mining system which addresses the above opportunities for enhancing learning and teaching. The system adopts the bilingual text-mining technique for analyzing learner-generated text on Moodle. It incorporates a bilingual taxonomy of domain-specific keywords for an automatic identification and counting of matching keywords in learner-generated text, and provides hierarchical visualization for an informative and interactive display of counting results of matching keywords. For learners, the bilingual text-miming results serve as prompt and relevant feedback on the progress and weaknesses of their learning. For teachers, the bilingual text-miming results support them to efficiently and evidently understand the overall teaching effectiveness and make pedagogical decisions for learning mediations in their teaching. The system has potential to promote data-oriented decision-making for the benefit of teachers’ pedagogical decision-making and learners’ reflective learning engagement. Copyright © 2017 by TEA Conference 2018. All Rights Reserved.