An English vocabulary learning support system for the learner’s sustainable motivation
© Hasegawa et al.; licensee Springer. 2015
Received: 29 September 2014
Accepted: 4 January 2015
Published: 27 February 2015
In English vocabulary learning, continuation is an important factor; however, many learners are not good at continuing learning because they tend to prefer amusement or rest. Our proposed system is targeting learners who are eager to learn but are not able to continue learning for various reasons. We especially focused on English vocabulary learning, and described an approach for learners who have difficulty with continuing learning. Our developed application aggressively supports the learners’ sustainable motivation by gamification techniques and an efficient difficulty setting method.
The term “electronic learning,” that is, “e-learning,” has become popular since the mid-1990s, and its goal is to create a community of inquiry independent of time and location through information and communication technologies Garrison (2011). Currently, companies and researchers are developing various types of e-learning systems. Because of the recent remarkable popularization and improvement in mobile devices all over the world, many people carry their smartphone on a daily basis; therefore, they can easily use ICT-technologies such as a telephone call, e-mail, accessing the internet, and various applications at all times. Accordingly, considering an e-learning environment where mobile devices are used, the term “mobile learning,” or m-learning, is also focused on Cavus and Ibrahim (2009). According to a practical study Evans (2008) which utilized m-learning in the form of podcasting, students believe that the podcasts are more effective than their textbooks as revision tools, and that they are more efficient than their own notes in helping them to learn. We considered that using a smartphone is effective for independent learning such as English vocabulary memorization because a learner can use the smartphone anytime and anywhere when the learner has only a little time. In English vocabulary learning, continuation is an important factor, although, many learners are not good at continuing learning. Among the learners who are not good at continuing to learn, there are two types of learners: (1) learners who have little motivation to learn; (2) learners who cannot resist the temptation as an amusement, even though they have motivation to learn. In this study, focusing on (2) learners, we developed an English vocabulary learning application for the learner’s sustainable motivation by gamification and cloud intelligent techniques utilizing characteristics of smartphones.
E-learning has an advantage that a learner can learn alone anytime and anywhere; however, it also has a disadvantage that it is difficult for the learner to maintain his/her motivation. In this section, we clarify a position and a characteristic of our study, through considerations of related works which enhance the learners’ motivation in the e-learning environment.
In order to maintain the learners’ motivation, we focused on gamification techniques. Gamification is not to create a video game, but it means techniques that are used in creating games for amusing the users. Utilizing the techniques in various scenes can enhance the player’s motivation Deterding et al. (2011). A SNS service Foursquare a succeeded to enhance the users’ motivation to check in their location to the service through gamification techniques that each user can get some points and badges when checking in. In addition, Preira et al. (2014) developed a smartphone application intended to change the user’s behavior through participating in the collaborative game. Therefore, we considered that utilizing gamification techniques in e-learning has a positive influence on the user’s learning motivation.
We considered that difficulty settings of questions are also important to maintain the learner’s motivation. In any subject, when most questions are very difficult, beginner learners will not be able to maintain their motivation owing to their pains caused by that they cannot answer most questions. In contrast, when questions are an appropriate difficulty for each learner, learners can feel a sense of accomplishment and self-growth. We expected that it enhances learners’ motivation. Among works related to the method of appropriate difficulty estimation, Chen et al. (2007) developed a system that recommends appropriate difficulty English articles depending on the learner’s English skill by estimating their skill using fuzzy item response theory (FIRT) on the mobile device.
In this study, focusing on the independent learning of English vocabulary memorization, we developed an m-learning application utilizing gamification techniques for keeping the learner’s motivation. Moreover, we proposed a new method, which estimates appropriate difficulty questions.
Proposal system design
Basic functions including implementing word utterance, incorporating synonym, antonym, and example sentence.
Growth character system.
Four levels distributed depending on each word’s difficulty.
Question selection by estimating the learner’s skills.
Time trial challenge and ranking system.
Visualization of learners’ efforts and degree of memorization.
We describe the details of these functions in the following sections.
If we create an enjoyable game with a few learning features for keeping learners’ motivation, learners who like video games may continue learning enjoyably. For example, in the RPG, a player can operate a character, can battle enemies, and can collect equipment; further, the player often needs to answer some English vocabulary questions to continue with the story. This idea may be effective for learners who have little motivation to learn; however, the game with a few learning features has disadvantages for our concept as follows:
(1) it increases wasteful time for learning; (2) learners will get tired of learning if the learners get tired of the video game; (3) the pain of learning in itself is not decreased; therefore, in our application, we selected simple English vocabulary learning in a repetition style with some gamification factors.
This is one of the interactive activities in our system for identifying the level of learners’ efforts. The objective of this system is to stimulate learners’ motivation by setting the other objectives such as games.
The second factor is the time trial question and ranking system. In the time trial system, learners can attempt to answer as many questions as possible correctly in a minute. After their attempt, our system shows the clear points depending on the accuracy rate, the number of answered questions, and the difficulty of answered words as illustrated in Figure 2 (c). The information in the middle of Figure 2 (c) indicates a result; the top line indicates the number of answered questions, the second line indicates the percentage of correct answers, the third line indicates difficulty of questions, and the bottom line indicates points gained depending on these results. The learners can gain satisfaction by overcoming their previous score; further, they can feel the improvement of their skills and efforts. Furthermore, this clear points are also reflected in the ranking, which enhances learners’ motivation through their competitive spirit. This application implements the function for weak point learning at the learners’ pace to steadily enhance their skills.
The last factor is SNS connectivity. In this application, learners can submit their clear points to SNS services easily. The learners can satisfy their desire for recognition from others, and this creates a rivalry with others.
Visualization of learners’ skills
Implementing of gamification is expected to maintain learners’ short-term motivation; however, for keeping their long-term motivation, it is desirable that the learner discovers a pleasure or a purpose of learning in itself. Many learners have difficulty with memorization learning such as English vocabulary learning. In our application, we visualize each learner’s efforts to make the learner’s growth and the increase of knowledge much more recognizable.
Visualization of learners’ efforts
This application shows a learner’s efforts such as the number of answered questions, the number of correct answers, and the difficulty of learned questions. The graph upper left in Figure 2 (a) shows the learner’s efforts value that is the sum of the points the learner gained each day in one week.
The learner can confirm the degree of his/her efforts using numeric values and graphs which prevents the decrease of motivation.
Visualization of learners’ skill and the degree of memorization
In desk learning, to date, it has been a problem that learners have difficulty in understanding their improvements
: how many words the learner can answer correctly, in this case.
However, utilizing e-learning, which can record and analyze all answers of each learner, our application can visualize the degree of the learners’ mastered knowledge. Our application shows the rate of mastered words and the average rate of correct answers as illustrated in the blackboard at Figure 2 (a). In Figure 1 (c), to visualize the learner’s mastered words and unmastered words, our application shows the degree of memorization for each word by progress bar. The progress bar indicates the percentage of correct answers. If the learner could answer correctly at the last challenge, it indicates full. For example, the progress bar of “abstract” is full because the learner answered it correctly at the last challenge of “abstract”, but the progress bar of “appreciate” is not full because the learner answered it incorrectly at the last challenge of “appreciate”. Our application cannot assess whether the learner has completely memorized each word; therefore, we deemed that the learner memorized the word when the most recent challenge was answered correctly. The progress bar in the upper right portion of Figure 1 (c) is the rate of mastered words, calculated by dividing the number of mastered words by the total number of words. Because the learners will be able to understand their weak points easily, learners can learn efficiently by themselves.
Question selection by estimating learner’s skills
Classifying English words based on the degree of similarity between each learner
An example of the learning history database on cloud (T:True F:False)
The ratio of unmastered questions to maintain the learners’ motivation
All words in our application can be classified as one of the following four kinds: mastered word, unmastered word, estimated mastered word, and estimated unmastered word. Using answer history data, our application can assess whether a learner has already mastered a word when the learner has challenged the word in our application. When the learner could answer the word correctly on the most recent attempt, the word is classified as a mastered word. When the learner could not answer the word correctly at the last time, the word is classified as an unmastered word. Using the above-mentioned method estimates whether the learner can answer the word correctly. When our method estimates true, the word is classified as an estimated mastered word. When our method estimates false, the word is classified as an estimated unmastered word. It is generally said that a rate of over 80 % known words in long sentences is good for sustainable motivation when reading long English books. Our application sets questions with the same ratio of the above-mentioned four kinds of words because our goal is to support English vocabulary learning with sustainable motivations; therefore, the ratio of mastered words is 25%, the ratio of unmastered words is 25%, the ratio of estimated mastered words is 25%, and the ratio of estimated unmastered words is 25%. Because unmastered words have already appeared one or more times despite incorrect answers at the most recent attempt, assuming that the learner knows unmastered words, the sum of the ratio of the known words is 75%. The learner can review and learn new words in this ratio with sustainable motivation.
Experience to evaluate accuracy
A comparison between the recall, precision and F-measure
The evaluation result
Right data (Acutually answered data)
Focusing on the accuracy of each, this result indicates that our proposed method could estimate, from 10 extracted words, whether or not about 90 words have already been mastered with 82.7% accuracy. However, because of the bias, if we estimate all results as true, the accuracy was 80.6%. Although the accuracy of our method is a little higher than the acuracy of all true, in terms of F-measure, our proposal both F-measure (T) and (F) was better than them.
Our application evaluation
Contents of the questionnaire and their results
Neither agree nor disagree
Weak point learning
1. I may learn on this app with sustainable motivation.
2. I enjoyed playing this app.
3. I felt that I could memorize English words.
4. I want to play this app once again.
Time Trial Challenge
5. I may learn on this app with sustainable motivation.
6. I enjoyed playing this app.
7. I felt that I could memorize English words.
8. I want to play this app once again.
9. I may learn on this app with sustainable motivation.
10. I want to grow the character more.
11. I may learn on this app with sustainable motivation.
12. I want to play this app more times to raise my score.
13. I may learn on this app with sustainable motivation.
14. I may submit my score when I get a good score.
According to these results, many participants answered “Agree” for much of the content; however, we focus on some distinguishing points. First, between content no.2 and no.6, time trial challenge receive more “Agree” responses than weak point learning; however, it also receive more “Disagree” responses. We considered that the learners felt that time trial challenge was more characteristic than weak point learning because the number of answers of “Neither agree nor disagree” in the time trial challenge was significantly fewer than that in the weak point learning. Therefore, time trial challenge has a good influence on learners’ sustainable motivation for some learners. Second, about content no.11, the ratio of “Agree” was 73.1% which was the highest ratio of all; therefore, most learners are especially interested in the clear points and ranking function. Finally, about content no.13 and no.14, both these contents have a lower ratio of “Agree”; further, the ratio of “Disagree” in no.14 was 61.5% which was the highest ratio of all; therefore, most learners were not interested in submitting their score to SNS services, and they thought that the SNS function did not affect their motivation in this case.
Because our application got many “Agree” answers, we considered that the gamification techniques had a good influence in some functions. In this paper, we found out that learners were especially interested in the clear points and ranking function, and were not interested in SNS submitting. We do not believe that the SNS function is not good for sustainable motivation, but we believe that a suitable utilization of SNS supports learners’ motivation; therefore, we should consider how to utilize SNS services to maintain learners’ motivation in future efforts.
In this study, focusing on motivated learners who cannot continue learning, we developed an English vocabulary learning support system for sustainable motivation. This application is equipped with the necessary functions for English vocabulary learning. In addition, we utilized information technology, such as gamification techniques, cooperation with SNS, and an unlearned words estimation based on the degree of similarity between each learner. Therefore, we believe that we have succeeded in making an application that supports the growth of self-sufficient learners in the following steps: (1) learner who cannot continue learning; (2) learner who enjoys our application in terms of gamification; (3) learner who is interested in self-growth; (4) self-sufficient learner.
This application is currently available on Android platforms 2.2 and above; further it is published in Google Play. As future efforts, we will evaluate the degree of continuation using our application. Moreover, to improve our proposed estimation accuracy, after gathering more usage data, we will consider parameters and estimation algorithms.
a Foursquare https://foursquare.com/
b Ke-Tan for TOEIC basic vocablary: https://play.google.com/store/apps/details?id=com.has.seelearning
c TOEIC vocabulary level check Vol.1 http://t-hase.rhcloud.com/
- Bachen, CM, Raphael C. Social flow and learning in digital games:a conceptual model and research agenda. In: Serious games and edutainment applications. London, UK: pp 61-84 Springer London: 2011.Google Scholar
- Cavus, N, Ibrahim D. M-Learning: An experiment in using SMS to support learning new English language words. Br J Educ Technol. 2009; 40:78–91.View ArticleGoogle Scholar
- Chen, CM, Hsu SN, Li YL, Peng CJ. Personalized intelligent M-learning system for supporting effective English learning. In: Conference proceedings - IEEE international conference on systems, man and cybernetics: 2007. vol. 6, pp 4898–4903 IEEE New Jersey, USA.Google Scholar
- Deterding, S, Sicart M, Nacke L, O’Hara K, Dixon D. Gamification. using game-design elements in non-gaming contexts. In: CHI ’11 Extended abstracts on human factors in computing systems: 2011. vol. Vancouver, pp 2425–2428 ACM NY, USA.Google Scholar
- Evans, C. The effectiveness of m-learning in the form of podcast revision lectures in higher education. Comput Educ. 2008; 50:491–498.View ArticleGoogle Scholar
- Garrison, DR. E-learning in the 21st Century. A framework for research and practice. NY, USA: Taylor & Francis; 2011.Google Scholar
- Herlocker, JL, Konstan JA, Riedl J. Explaining collaborative filtering recommendations. In: 2000 ACM conference on computer supported cooperative work.NY, USA: ACM: 2000.Google Scholar
- Jegers, K. Pervasive game flow: Understanding player enjoyment in pervasive gaming.Comput Entertain. 2007; 5(1):1–11.View ArticleGoogle Scholar
- Pereira, CV, Figueiredo G, Esteves MGP, deSouza JM. We4Fit: a game with a purpose for behavior change. In: IEEE 18th International conference on computer supported cooperative work in design.New Jersey, USA: IEEE: 2014.Google Scholar
- Simoes, J, Redondo RD, Ana Vilas F. A social gamification framework for a K-6 learning platform. Comput Hum Behav. 2013; 29(2):345–353.View ArticleGoogle Scholar
- Tan, PH, Ling SW, Ting CY. Adaptive digital game-based learning framework. In: Proceedings of the 2nd international conference on digital interactive media in entertainment and arts. NY, USA: ACM: 2007. p. 142–146.Google Scholar
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