Applying Personalized Learning in Gamified e-Learning to Enhance Motivation and Effectiveness in Secondary Language Vocabulary Learning

There is a constant rise and demand for e-learning today using computers and smartphones. As shown in Figure 1, it is forecasted that the Mobile Learning Industry is experiencing a huge market growth from $3.37 billion in 2011 up to $37.60 billion by 2020 (Docebo, 2016). This growing trend in mobile learning spending shows that more and more people are now interested in learning through a device rather than the traditional approach of learning in schools and classrooms.

Figure 1. Mobile learning market trend (in USD Billion)

The increase of mobile learning products comes with various teaching and technological innovations such as gamification. Gamification in e-Learning uses various game mechanics and theories in non-game situations in order to make learning more   engaging and motivating for students. In fact, e-Learning is still the largest domain (17.6%) of trending journal topics within the utilization of gamification as shown in Figure 2 (Kasurinen, 2017). In a research, out of the 1164 scientific papers regarding gamification, MOOC (Massive Open Online Courses) ranked the highest for the most individual topic related papers written within the application of gamification.

Figure 2. Gamification related papers from 1164 papers

In regard to gamification learning, there exists similar models such as game-based learning and serious games. Game-Based Learning (GBL) is the process and practice of learning using games from the learner’s point of view through environments and elements outside of the standard school learning systems (Huizenga et al., 2019). Serious Games, on the other hand, are actual games designed to help teach something new rather than for purely entertainment purposes (Malaquias et al., 2018). The core distinctions of the definition, purpose, and focus within these three models are highlighted in Table 1. The proposed model used in this research focuses on the implementation of gamification within e-Learning.

Table 1. Different models combining Gaming and Learning

Due to globalization, there is an increasing number of language e-Learning sites implementing gamification. The current models of Gamification in secondary language E-learning focus heavily on applying classical extrinsic game-like elements such as levels, challenges, point systems, and rankings to create a user interface that rewards students who complete certain assignments (Park, 2014). Popular language learning site like Duolingo has already integrated ranking systems and virtual shops where students can purchase with the app’s native virtual currency in order to increase user experience and motivation.

While these gamified elements may produce short-term motivation and engagement to a certain point, research has shown that external rewards, over a long period of time, tend to eventually undermine students’ internal motivation in engaging with the learning materials (Kim, 2017). This is because once these external stimuli disappear so will the students’ motivation along with it. Thus, the shortcomings of the current gamification model in e-learning is the lack of personalized learning in developing internal motivation that influences an effective learning outcome.

Personalization in online language learning means to optimize in multiple fields such as the use of language, content, context, and user interface that best suits for the students. Following one personalization learning principle, Mayer (2009) concludes that students learn better online when the computer platform uses personalized colloquial language rather than a neutral or formal language. Humanizing a computer task makes the learning experience more relatable and thus enjoyable. Testing this principle, Reichelt (2014) conducted an experiment with college students studying online and resulted that personalization within e-learning has had positive impact on the students’ overall motivation and retention.

Ownership of learning is often associated with the self-reference effect (SRE). This self-reference effect links ideas with the self and create self-processing biases to enhance memory, attention, and perception for these ideas (Humphreys, 2016). In an educational context, this effect can be a useful memory enhancer for the students as they are more likely to remember their own experiences and ideas rather than those of others (Cunningham, 2017). In their research experiment of memory recall with 7 to 9 years old (Turk, 2015) concluded that having ownership of learning enhances one’s retention rate of information which greatly aids in learning secondary language spelling and vocabulary.

While current models of personalized e-learning exist such as myPTutor and Personalized Creativity Learning System (PCLS) that incorporates AI and data mining techniques to create lesson plans, they still do not offer full autonomy and personalization for the students as the computer still plays a calculating guessing factor in the direction of the learning. These models only apply guess work and does not fully represent the needs of the students using the programs. Moreover, these models do not fully apply gamification elements and focus only on the context and content of learning without creating external stimuli to engage the students.

Despite this, few gamification research focusses on improving students’ internal motivation by applying personalized ownership of learning that caters specifically to the individual’s interests, needs, abilities, and learning styles. The focus still relies heavily on the gamification aspects in order to produce short-term motivation for the students.

Therefore, to make a more effective e-Learning model in language learning, a combination of gamification and personalized must be applied together in harmony.  Thus, this research aims to create and analyze a new hybrid model that incorporates both personalized learning and gamification element to create motivation to enhance the overall effectiveness of secondary language vocabulary learning.

Researchers: Kang Ha Sung and I Gede Putra Kusuma Negara