Research Article

Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model

Table 2

Preprocessed features along with their category and description.

Features’ categoriesFeaturesDescription

Behavioral featuresODGPOnline discussion group participation, i.e., the number of times a learner has participated in the online discussion group (numeric)
NPPNumber of times problem posted online related to study topics (numeric)
NPSNumber of times solved the posted problem online by other learners during learning (numeric)
NTAQTotal number of quiz attempts in three modules (numeric)
TRRLearning topic average repetition rate in three modules (numeric)

Context featuresASTAverage study time per learning topic (numeric)
NTRANumber of times text resource accessed during the learning process (numeric)
NVRANumber of times video resource accessed during the learning process (numeric)
MP1Learner performance in module 1, numeric: (18 to 20 = very good), (15 to 18 = good), (12 to 15 = average), (9 to 12 = satisfactory), and (0 to 9 = fail)
MP2Learner performance in module 2, numeric: (18 to 20 = very good), (15 to 18 = good), (12 to 15 = average), (9 to 12 = satisfactory), and (0 to 9 = fail)
MP3Learner performance in module 3, numeric: (18 to 20 = very good), (15 to 18 = good), (12 to 15 = average), (9 to 12 = satisfactory), and (0 to 9 = fail)
APVAcademic places visited. Degree of academic places visited during the learning process, i.e., library and classrooms, numeric: 0 to 1
SPVSocial places visited. Degree of social places visited during the learning process, i.e., playgrounds and hostels, numeric: 0 to 1

Final performance featureFGFinal grades showing the final performance of a learner in three modules, categorical: (18 to 20 = very good), (15 to 18 = good), (12 to 15 = average), (9 to 12 = satisfactory), and (0 to 9 = fail)