The Text Mining Model for Lecturer Performance Evaluation: A Comparative Study

To support the evaluation of the teaching and learning process in higher education institutions, it is necessary to develop a text mining (TM) model. The aim of this research is to compare the performance of Long Short-Term Memory using Word Embedding Text to Sequence (WETS-LSTM), WETS-BiLSTM, WETS-CNN1D, and WETS-RNN, using four dataset categories including pedagogic, professional, personality, and social competency. This research has five main steps, including literature study, dataset collection, TM model development, and evaluation. Dataset is collected from Universitas Sjakhyakirti, Institut Teknologi dan Bisnis Palcomtech, Universitas Muhammadiyah Palembang, Universitas Bina Darma, AMIK Bina Sriwijaya and Politeknik Darusalam. The questionnaire distribution process initially yielded 6,170 responses with 6,164 valid across four competency categories, with total of 24,656 text data for analysis. Model of WETS-LSTM obtained the best performance overall, achieved the train accuracy of 96.65% and the highest test accuracy of 82.92%. The CNN1D with Word Embedding Text to Sequence (WETS-CNN1D) demonstrated good train accuracy with 96.73% but obtained lower test performance with 80.67%. The WETS with Recurrent Neural Network (WETS-RNN) obtained the weakest results, with a train accuracy of 95.88% and a test accuracy of 77.99%.