Literature Review on Mobile Learning, Microlearning and Gamification

Context

       

     As a Special Education Math Teacher in Katy ISD, finding ways to improve the education of my students is an intentional role. Katy ISD is in the suburbs of Houston, Texas. This is my seventh-year teaching. I work as a junior high teacher at Beck Junior High. The total population is 1,259 students grades 6 through 8. The economically disadvantaged are 21% of the population. The Special Education population at Beck Junior High is 9.5% compared to 12% district average. The gifted and talented population is at 16.6%. Beck Junior high has an A accountability rating, the second highest level in Texas. The racial makeup is 45% white, 8% African American, 15% Asian, and 26% Hispanic. The junior high employs 72 teachers. The racial makeup of the teachers is 57% are white, 7% are Hispanic and 2% are black, 2% are Asian, and 6% are two or more races. The average teacher experience is 11 years. Staff that holds a bachelor’s degree is 82% of the population and those that hold a master’s degree is at 19%. Lastly the student to teacher ratio for the school is at 18 students per teacher.  My case load is made up of the 100 students who qualify for Special Education services. My aim is to improve their mastery in their math course through supplemental or replacement education.

 

Rationale

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       During the last year, the Covid pandemic has uprooted teaching as we know it. Students and teachers have had to transition from traditional schoolwork to variations of virtual education. This last year has made it imperative to expand teaching through technology. As a teacher my goal is to be able to provide the best options in instructional choices for brick-and-mortar learning as well as virtually. How well can students learn through mobile applications, microlearning, and gamification? This literature review will look at these specific learning affordances and specifically aimed at Special Education populations. Mobile learning has shown to have the ability to increase motivation among students and learning. Micro-learning is currently effective and used as learning strategy in the corporate world (Nikou & Economides, 2018).  Initial results indicate that supplementing learning through mobile-based microlearning students showed improvement in “basic psychological needs of self-perceived autonomy, competence, and relatedness and improved students exam performance in terms of factual knowledge” (Nikou & Economides, 2018).

 

       One other possibility is to explore the positive affordances that gamification can have on education. One research study using at risk students in the special Education population showed promise for motivation and engagement. “Educational games can be highly useful in a learning context” (Halbach & Solheim, 2018, p. 255).  A meta-analytic examination by Traci Sitzmann showed participants learned more from games than the control group as well as increased self-efficacy and retention (2011, p.513).

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Literature Review

       

The purpose of this literature review is to explore the instructional strategies of technology in K-12 education in mobile learning, microlearning, and gamification. What benefits and pitfalls does technology in the classroom hold? Currently 80% of the population have a smart phone and 1/3 of YouTube videos is educational. “Mobile learning is no longer focused directly on apps but instead connectivity and convenience with the expectation that learning experiences will include mobile-friendly content, multidevice syncing, and anywhere/anytime access” (Alexander et al., 2019). The benefits of mixing face-to-face and online combines varying ways to engage learners. There are positives for the student and the instructor with the use of mobile learning, microlearning, and gamification.

 

Mobile Learning

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      Mobile learning is also called M-learning. M-learning is a type of learning that takes place through portable and handheld mobile devices. Mobile learning allows learners to learn on the go and to search topics based on just in time needs no matter where they are (Hong Ng, Keng Koh & Ling, 2020). Much of the population has access to a smart phone or mobile device and there is increasing trends towards responsive learning (Alexander et al., 2019). Other benefits include increased motivation, engagement, and increased self-motivation.

 

     As of February 2021, 85% of the U.S. population has a smartphone (O'Dea, 2021). With as many smart phone users as there are Briz -Ponce et al., (2017) investigated the factors that could influence mobile learning. They included 160 university medical students who were ages 18-21. Most participants were female and were in their third year of medical school. A large portion of the participants, 63% had use of both a smart phone and a tablet. The study aimed to measure anxiety, attitude, perceived ease of use and usefulness, reliability, self-efficacy, and social influence when it comes to using mobile technology for learning. The instrument used was a 5-point Likert survey with 53 questions.

     The results of the study show that as technology increases there is greater potential for mobile learning to be used in curriculums. The survey showed that students who viewed mobile devices with perceived usefulness also in turn had a positive attitude. The students had a strong positive attitude with 57% and 40% would recommend it to others. However, students were less enthusiastic about adopting it. Overall learning through mobile technology was viewed positively. Some criticisms of this study are that they focused solely on medical graduate students. The study also did not include any type of intervention or a control group but focused only on students’ perceptions of mobile learning with quantitative data only.

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     Technology has become increasingly important in education. Yang, Carter, Zhang & Hunt (2021) went out to explore how each state implemented ESSA (Every Student Succeeds Act) plans and what themes were used in blended learning. ESSA was signed into law in 2015 to ensure equality and accessibility to technology for all students. They collected data from 50 states and their corresponding ESSA plans. All data was open to the public and found on the state’s education website.  With increasing knowledge in varying student blended learning needs Yang et al. (2021) incorporated many citations for reasons blended learning and use of mobile devices improves educational outcomes. Data was analyzed with TA and identified, analyzed, and reported on themes across the data. The data went through six phases (TA): familiarize, code information, search for themes, review themes, define themes, and produce report.

    The findings showed that there are 12 states that have included plans to facilitate technology in the classroom. Five states included mobile technology into their professional development. Four states stated they were using mobile technology in authentic learning experiences outside of the normal brick and mortar classrooms. There were 33 states who did not provide any information on mobile technology in the classroom. Lastly, there are several states that reported that they are taking steps to improve their classrooms with the latest findings.  While the study covered overall state themes on mobile learning there is still limited research in K-12 schools for mobile learning. Also, data was taken only from the United States and might not apply cross culturally.

     

      Anshari et al., (2017) sought to determine the usefulness of a smartphone in the classroom.  Are smartphones enhancing learning or hurting learning? The study took place across schools in universities in Brunei. The participants were students who use their smartphones regularly in class to access class material, interact with teachers and complete group assignments. Male participant statistics were 49% while 51% were female. Majority of the students were undergraduates, then followed by high school and junior high. About 70% of the students were ages 20 or younger. There were 355 students surveyed across all parts of Brunei in March 2015. Students were randomly chosen from this group to participate in focus group discussions. Qualitative data was collected from the focus group discussions as they were open ended questions based off mobile learning in the classroom.

      After analyzing the data Anshari et al. (2017), found that 80% of participants used their mobile device to access learning tools. They concluded that smartphones can be a convenient and portable learning aid. Students reported using WhatsApp for communication with friends as their most used app. Other popular applications on the smart phone were for music or video. Based off the survey 67% of participants preferred to use mobile technology for learning. Overall, findings showed smart phones can be a learning aid, give multiple sources, help with multitasking, and is environmentally friendly. Negatives associated with using the mobile device for learning are that they can cause distraction, dependency, lack hand on skills and reduce the quality of face-to-face time. This study used a mixed methods approach and triangulation validity to make their final inferences.

 

     Hong Ng, Keng Koh & Ling (2020), investigated how to incorporate new literacies in a framework for mobile learning. The study looks at mobile environment issues, learning contexts, learning experiences, and learning objectives with students struggling academically. Phase one participants included 61 students ages 14 to 16 years old and seven teachers in Singapore. Phase two consisted of 34 students between the ages of 14 to 16 and included seven teachers from four schools. Qualitative data was collected over 2 years. Hong Ng, Keng Koh & Ling (2020) interviewed seven teachers and led focus group discussions with 95 students. Open ended questions were asked about their views of working with EBS (Elements of Business Skills) and the learning and usefulness of the mobile application. The teachers incorporated the classroom application for students to have access to their textbook, notes and classroom leaderboard. The students were used to inform the app and make it more useful in the classroom. There was a leaderboard to engage students as well as a discussion board.

      The teachers affirmed that mobile learning was most effective when distributed in bit size pieces. Students perceived using the application as positive in that learning could take place anywhere or anytime. The app used more pictures based off feedback on cognitive overload. Also, students wanted more video content available in the application. Based off feedback from the students, text curriculum was downsized to smaller five-to-10-minute readings and supported by media. Some other important pieces for success as a classroom application was that students could work collaboratively and communicate through the app. Overall, Students and teachers like using the app and students particularly liked the gamification and competition. The app also facilitated collaboration between students. Throughout the development students wanted a more blending of multimodal texts.   This study would have been better had there been a control group and included some quantitative data such as end of year exams.

 

      The Briz-Ponce et al. (2017), study shows that student attitudes are relatively positive towards the use of mobile technology in the classroom. While this study focused solely on attitudes it is an indicator that early adopters can help accelerate the change (Spector, 2016). Yang et al. (2021) studied showed that states are recognizing the need for transformative change in the classroom when it comes to mobile learning. Blended learning and online learning have had a positive impact on education and through the ESSA act schools are adopting change. Anshari et al., (2017) used convenience and random sampling for an initial survey. They followed up randomly with participants through group discussions. The results of their positive findings over use of the mobile devices across education were validated by both quantitative and qualitative data. Hong Ng, Keng Koh & Ling (2020) used qualitative data to discover the benefits of using a mobile device in the classroom. A critique here would be that it may have been possible to find a control group in another school to compare posttest scores. Overall, the study found that students found the leaderboard to be motivating and had positive attitudes towards the mobile application.

 

Microlearning

     

      Microlearning deals with small learning units related to e-learning.  As instructional technology microlearning can be designed to be delivered in small chunks in timely intervals. “Learners make use of micro media to learn the microcontent” (Nikou &Economides, 2017, p. 270). Mobile based microlearning becomes accessible anytime, anywhere, can be adaptive, on demand and learner centered (De Gagne et al., 2019). According to the cognitive load theory, microlearning enables participants to have long-term memory of learning materials by constructing small structures repeatedly.

 

      De Gagne et al., (2019) set out to review microlearning on education and the pedagogical implications associated with it. 3096 references were reviewed across multiple countries and disciplines. Criteria as a basis of learning included a timeframe of 15 minutes per microlearning activity. Types of microlearning included podcasts, short messages, microblogging and social networking.

Results were focused on the Kirkpatrick levels of evaluation. There were 17 studies that qualified with focus on technological uses with microlearning. All outcomes were assessed by using the Kirkpatrick model. Level I outcome where the study showed a positive reaction to the learning was represented in 94% of studies. Level II outcome where the study showed that microlearning can increase performance with 82% of the studies. Lastly, 29% of the studies showed that microlearning had a positive effect on student behavior, Kirkpatrick level III.  There have been no studies that studied the highest level of the Kirkpatrick model, results. Microlearning is an educational strategy and has a positive effect on retaining information, engagement and studying. This is a meta-analysis of microlearning. Most of the studies were done in the graduate school and did not combine information from grades K-12.

 

      Nikou and Economides (2017) studied both mobile and micro-based learning. With the rise of bite sized learning units with mobile delivery in the workplace, Nikou and Economides (2017) set out to see if the benefits also applied to K-12 students. The lens of the paper was through Self Determination Theory. Self Determination Theory is that through autonomy, competence, and relatedness student will be intrinsically motivated. The study focused on homework within the science classroom. The participants were 108 high school seniors who live in Europe. Average age was 17 years old. Male and Female participants were evenly divided. Students all had mobile devices and scored 82% on mobile self-efficacy survey. All participants had the same STEM teacher and were divided into to two equally academic groups.  The control group had 54 students and the experimental group had 54 students. The instruments included a pre-questionnaire, pretest on knowledge, post-test on mobile competence, post-test on factual knowledge, and survey on learning satisfaction. The intervention for the experimental group included mobile microlearning homework assignments [JF1] while the control group continued with traditional homework. Pretest and posttest were created by the instructor along with two subject matter experts for greater content validity.

All participants were given identical instruments, as well as identical classroom learning and teaching. The experimental group completed their homework via mobile device in micro unit segments. The control group was given homework in traditional pen and paper. Performance on the pretest between the two groups showed no statistical difference. The results of the study show that there were significant learning gains for the experiment group. Students in the experimental group showed higher learning achievement mean with an F value of 7.49 and 11% increase in posttest achievement. Those in the intervention group also increased significantly in perceived levels of autonomy, competence, relatedness, and learner satisfaction. Autonomy had an F value of 29.75, Competence had an F value of 14.35 and relatedness had an F value of 10.58. For those students that used the micro learning on their mobile device showed improvement in motivation. Lastly, learning satisfaction was also measured across the two groups and again the experimental group showed a t value of 3.35 over the control group. Overall, this study shows that mobile based microlearning increases student motivation and improves knowledge and learning satisfaction of high school students.

 

    Aitchanov, Zhaparov, & Ibragimov (2018) studied microlearning in the classroom and analyze the effectiveness and perceived usefulness by teachers and students to show that change is needed in the classroom. The authors quote the article “Numbers Don’t Lie: Why Microlearning is Better for Your Learners” stating that students are 50% more engaged and that there is 17% learning gain. Not only does it benefit the learners but costs less. The participants were 100 eighth grade students in Kazakhstan who are currently studying computer science. Two classroom teachers taught the same material for four weeks, one being the control group (50) and the other teaching through mobile microlearning activities (50). The instruments used was an end of course survey, test scores, and interviews.

     The results of the study show that students in the experimental mobile microlearning group had an average test grade of 74.5% while the control group had an average of 55.6% a difference of 18.9%. Also statistically significant is the mode of 100% for the experimental group. Also, out of the 50 students over 15 students scored a 91 or above. The survey taken also showed that students and teacher had a positive attitude as well as showed positive academic gains. This static group comparison design had no pretest to determine whether groups statistically differ. The threat to internal validity is in the differential selection (Mertens, 2020).

 

    De Gagne et al., (2019) sets out the research studies done on microlearning in education through a Kirkpatrick model lens. The results showed that most studies showed the participant reactions as positive towards their microlearning experiences. Another important factor in the analysis was that studies also showed significant improvement in learning. Missing from studies are the higher-level outcomes of behavior change and long-term results. Nikou and Economides (2017) set up a pretest-posttest control group design. “This design controls for the effects of history, maturation, testing, instrumentation, and experimental mortality” (Mertens, 2020). With this study microlearning as a small portion of the students learning (homework only) had significant achievement gains. With such a small tweak of the curriculum microlearning vastly improved the educational outcome. Another mobile microlearning quasi-experimental study showed learning gains over their control group counterparts. Aitchanov, Zhaparov, & Ibragimov (2018) results showed a difference of 18.9% in learning when using mobile microlearning in place of the traditional pen and paper.

 

Gamification

     

     Game based learning or the use of video games for educational purposes, has been shown to be an effective means of enhancing both learning motivation and academic performance (Kingsley & Grabner-Hagen, 2015).

Is game based learning appropriate for all students? Halbach and Solheim (2018), set out to determine whether gamification and microlearning has affordances to increase motivation and learning performance in middle school pertaining to students with cognitive disabilities. The study took place in Norway with six students, grades six and seven. The participants are four male students and two female students with learning disabilities. The instruments used to collect data was from the mobile applications tools themselves, HP5, mYouTube (or mobile YouTube) and Engage. The intervention itself consisted of student participation in five to seven lectures per day and 15-20 minutes total. There were two to five tasks per lecture. The lectures were in HP5 and mYouTube accessed through mobile devices. Engage is an application that measured the students progress through the applications. HP5 is a web application that can support a wide variety of content including interactive videos, quizzes, gamification and much more. During the ten-day process the application Engage was having technical difficulties and did not collect all of the data necessary. Qualitative interviews were then taken to help supplement the results.

Even with the missing data there was a 25% improvement in learning achievement. Interviews showed that students found the microlearning through the interactive applications motivating, positive and engaging. Some criticisms of the study are that data was not fully collected from the gaming program. There were significant time delays in the technology. It would be interesting to see this study done on a larger pool of participants and have the use of a pretest and posttest to measure the learning gains. The researchers of this study are optimistic that with greater technology gains, there will also be greater learning gains possible for the special education population.

 Ahmad (2018) explored online gamification with microlearning as an additional support to the learning process with mobile learning application. The aim of the study was to show how mobile microlearning improves motivation and enhances the learning process. There were two groups of students, one was the experimental group with the intervention and the other the control group with face-to-face instruction. The instruments to measure the intervention was a Likert scale survey before and after as well as the final exam that was given to both groups. The intervention consisted of an online game with 51 questions divided into micro chunks. Both groups covered the same material just in two different mediums.

The results of the intervention group had a 15% increase in achievement over the control group. The survey results that measured attitude also showed improvement in perceived learning, and method of instruction. Overall micro learning and gamification had a significant positive influence on exam results, learning outcomes and process of instruction. A threat to validity in this static-group comparison design is differential selection.

 

      Welbers et al., (2019) set out explore the outcome of provided detailed feedback versus generic feedback in learning gamification. Another aim of the study was to look at the binging properties that can come with gaming. The idea of gamification here was to motivate, engage, increase learning, and stimulate the mind (Kapp, 2012). For best learning results in gaming, Welbers et al. (2019), points out work that learning is more efficient in short intervals. The participants were new undergraduate students at a Dutch university between March and April 2016. Students were introduced to the app through e-mail, class announcement, and signage around campus. Two phases were conducted with 2444 students contacted in round one and 706 contacted in round two. A short survey was required before logging in, and 233 students completed it in the first round and 84 students in the second round. Majority of students were in the first year of undergraduate work and 84% were from the ages of 18-25. The first instrument was a five-point Likert survey over their knowledge if the campus. There were two groups of the game participants, 46 with no game limit and 55 with a game playing limit. Both groups were split almost equally on whether they received generic feedback or personalized feedback.

      Results showed that in general the male participants played more sessions than their female counterparts. Contrary to the authors hypothesis, personalized feedback did not encourage greater amounts of use, but had the opposite effect. Participants with generic feedback tended to play more often. Lastly, the results showed that limiting game play did not demotivate students into playing on another day. This is significant as limiting game play could help teachers limit game play as to spread out the learning over time. The participation rate of the study was relatively low. The application was not connected to any required work but rather possibly viewed as extra work. While this study does target gamification, it did not look at capturing the learning or motivational benefits.

 

      Halbach and Solheim (2018) focused their study on students with cognitive disabilities but found after implementing the mobile microlearning and gamification there was a 25% improvement of learning. While there was not a control group there was individual learning gains with participants and teachers reporting positive perceptions of the technology.

 

     Ahmad (2018) combines all three categories using mobile technology with microlearning and gamification. The results of his study were overwhelmingly positive. The experimental group had 15% learning gains over the control group and was positively viewed by teachers and participants. Welbers et. al., (2019) explored gamification on providing essential information for freshman students. While there many students solicited for the gamification app an exceedingly small pool of students chose to follow through. The results of the study showed that personal feedback is not required to keep students motivated in the microlearning gamification and that setting time limits does not adversely affect use. Overall, the results from these studies show that gamification improved learning over traditional methods is positively perceived as useful and engaging.

 

Implications

 

      At the forefront of the strengths is the flexibility that mobile learning allows for the learners and instructors. Mobile learning allows for greater geographical distances between persons, allows for 33-55% less seat time, and aligns with the learner needs. Students reported increased satisfaction, and increased knowledge (Smyth et al., 2012). Based off a study done across 100 K-12 schools “They found that students became more motivated and self-directed with their learning, and teachers who adopted the use of held more positive perceptions (Yang, 2021).  The benefits of microlearning are that it is in small bit sized pieces. By taking information into small chunks, it makes it easier for learning retention (Nikou & Economides, 2017). Microlearning can also be combined easily into a more flexible learning schedule and meshes well with mobile delivery. These studies and others also infer that microlearning and mobile learning provide increased motivation and autonomy in the classroom. Mobile, microlearning, and gamification all have the capability to include social aspects. By including social aspects in the blended learning environment not only is motivation, autonomy a biproduct but also a students sense of relatedness or social presence (Nikou & Economides, 2017).

            There are some barriers the instructors must maneuver around when conducting a blended learning environment. The main barrier to success is technical difficulties as seen in Halbach and Solheim (2018). Other learner difficulties for instructors to consider are self-isolation, time management, invasiveness of blended learning, self-regulation, and lack of non-verbal cues (Smyth et al., 2012).

Some Caveats to the research of mobile, microlearning and gamification is that there is limited data on K-12 students. Most research studies were with 18 and older students (Yang et al., 2021). Any transformative impacts on education for K-12 would need more evidence. The literature is also limited in what design features are imperative for positive learning outcomes. While students still face challenges when it comes to mobile learning the main structure needs to be made up of social interaction and autonomy.

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References 

 

Ahmad, N. (2018). Effects of gamification as a micro learning tool on instruction (pp. 1–9). E-Leader Bangkok 2018. Retrieved June 18th, 2021, from         https://www.g-casa.com/conferences/bangkok18/pdf_ paper/Ahmad%20Efects%20Gamifcation.pdf

 

Aitchanov, B., Zhaparov, M., & Ibragimov, M. (2018). The research and development of the information system on mobile devices for micro-                   learning in educational institutes. In 2018 14th international conference on electronics computer and computation (ICECCO) (pp. 1–4). IEEE.

 

Alexander, B., Ashford-Rowe, K., Barajas-Murphy, N., Dobbin, G., Knott, J., McCormack, M., … Weber, N. (2019). EDUCAUSE Horizon Report: 2019        Higher Education Edition (Links to an external site.). 44. Retrieved from: https://library.educause.edu/-/media/files/library/2019/4/2019horizonreport.pdf

 

Anshari, M., Almunawar, M. N., Shahrill, M., Wicaksono, D. K., & Huda, M. (2017). Smartphones usage in the classrooms: Learning aid or                         interference? Education and Information Technologies, 22(6), 3063–3079. https://doi.org/10.1007/s10639-017-9572-7

 

Briz-Ponce, L., Pereira, A., Carvalho, L., & Juanes-Mendez, J. A. (2016). Learning with mobile technologies - students' behavior. Computers in                 Human Behavior, 72, 612–620. https://doi.org/http://dx.doi.org/10.1016/j.chb.2016.05.027

 

De Gagne, J. C., Park, H. K., Hall, K., Woodward, A., Yamane, S., & Kim, S. S. (2019). Microlearning in health professions education: scoping review.       JMIR Medical Education, 5(2). https://doi.org/10.2196/13997

 

Glover, I. (2013). World Conference on Educational Multimedia. In play as you learn: gamification as a technique for motivating learners (pp. 1999–       2008). Chesapeake, VA; Hypermedia and Telecommunications.

 

Halbach, T., & Solheim, I. (2018). Gamified micro-learning for increased motivation: An exploratory. 15th International Conference on Cognition           and Exploratory Learning in Digital Age. https://files.eric.ed.gov/fulltext/ED600597.pdf.

 

O'Dea , S. (2021, May 12). Percentage of U.S. Adults who own a Smartphone From 2011 to 2021. Statista.                                                    https://www.statista.com/statistics/219865/percentage-of-us-adults-who-own-a-smartphone/.

 

Nikou, S. A., & Economides, A. A. (2018). Mobile-based micro-learning and assessment: Impact on learning performance and motivation of high           school students. Journal of Computer Assisted Learning, 34(3), 269–278. https://doi.org/10.1111/jcal.12240

 

Smyth, S., Houghton, C., Cooney, A., & Casey. (2012). Students experiences of blended learning across a range of postgraduate programmes.             Nurse Education Today, 32, 464-468.

 

Spector, J. M. (2016). Foundations of educational technology: integrative approaches and interdisciplinary perspectives. Routledge.

 

Yang, S., Carter, R., A., Jr., Zhang, L., Hunt, T., (2021). Emanant themes of blended learning in k-12 educational environments: Lessons from the            every student succeeds act. Computers & Education, 163.

           

 

 

 

 

 

 

 

 

 

 

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