{'fraction_non_alphanumeric': 0.02810304449648712, 'fraction_numerical': 0.0, 'mean_word_length': 6.122222222222222, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Learning Personality Modeling for Regulating Learning Feedback

Technological affordance has broken down the time-space boundary of learning. This has resulted in a shift of control (e.g. Learning pace) and responsibility onto the learner. The learning personality of learners therefore plays a more critical role in influencing learning effectiveness. Furthermore, a Virtual Learning Environment (VLE) will likely comprise different users, rendering a linear teaching method insufficient. User's attitude influence effectiveness of learning and negative emotions arising from mismatched learning personality to feedback may hamper learning, different learning personality requires different presentation of information. The proposed Complementary Personality Agent (CPA) framework focuses on predicting and assessing user's learning personality by deriving information from user's interaction, matching it to learning personality traits (dependent or independent) and regulate learning feedback (e.g. Hint-feedback or frequency). Generated feedback is used to aid learners towards task-goal accomplishments and is constantly examined for effectiveness by evaluating the learner's state of emotion using a simplified version of the Pleasure, Arousal, Dominance (PAD) emotion model.