Abstract:
"English is one of the languages spoken by most people worldwide, making it recognized as a
language of international communications. Although Sinhala is the primary language of Sri
Lanka, fluency in English is equally valued, particularly for those starting in the workforce.
English proficiency greatly increases employment opportunities in almost every employment
field. University students can be identified as a significant portion of the population, whose next
move is to begin working in the sector. For them, knowing English fluently is therefore a must.
Hence this research was carried out to design and implement a mobile learning system to improve
the English-speaking proficiency of university students since communication skills are more
crucial for an employee.
This m-learning system has two main parts which are the mobile application which is capable of
running on both Android and iOS mobile devices and the recommendation algorithm which is
implemented to recommend learning activities to the user. The activities are suggested to that
particular user according to the user’s preferred learning style based on the VARK model. This
recommendation algorithm is a collaborative filtering algorithm and data which is needed for the
algorithm were collected through a survey among the university students of Sri Lanka.
As the initial results, the model was capable of recommending 5 activities to the user from the
total activities when a user initially selects an activity based on his preference. This is done as
per the ratings test users gave for each activity during the survey, by calculating the similarities
of ratings. The model obtained a Mean Square Error of 0.25 during the process."
