LDT 505 Lesson 8
My goal this week was to step a bit back from my granular analysis of mobile technologies in language learning to see the bigger picture, contemplating my audience from a demographic perspective while considering the purpose of technology I plan to integrate into my learning environment. In other words, put language acquisition on the back burner and focus on what it means to be an adult learner while exploring the design and functionality of a portal as a learning tool.
The adult learners in my ESL program range from 18-65 years of age. As many of the articles I’ve read recently have focused on high school or college students, I was excited to read Mature students using mobile devices in life and learning to gain a better understanding of the students on the more mature end of the spectrum. An 18 year old and a 65 year old can certainly have different perspectives on both learning and use of technology. I was slightly disheartened when I realized the students highlighted in this research were masters and doctoral level students whereas the level of education obtained by my students is generally high school level or some college. While the demographics were not 100% on par, some interesting points can still be taken away from this study. The first interesting point of reflection is that “age has a bearing on behavior in terms of learners’ experience and ability to reflect on that experience” (Kukulska-Hulme et al., 2011, p. 19). In our program, we have seen learners’ attitudes towards authority figures and mobile learning technologies vary greatly depending on age. Younger adults are more likely to display behaviors that are self-serving and challenge authority while mature learners are more acquiescent with behaviors geared toward maintaining a positive collective environment. While in general both young and mature adult learners display a positive attitude toward mobile assisted language learning (MALL), low levels of perceived ease of use are more likely to be a deterrent to mature learners for accessing mobile tools. My second point of reflection is the role of mobile devices in “making productive use of downtime” (Kukulska-Hulme et al., 2011, p. 31). My learners bounce from night shift to morning classes to snoozing for a few hours to picking up kids from school to preparing dinner, then repeat it all again. Sometimes there’s overtime at work. Sometimes there’s a second job. There is little downtime. What tools work best for learners on the go? What can be reviewed or learned in a 10 or 15 minute setting? How can my learners optimize those 15 minutes? Perhaps reviewing vocabulary on Quizlet, perhaps recording their voices in a voice recorder to practice pronunciation, or perhaps practicing listening to language in context in Voice of America. Instructor guidance can help learners determine their best learning path. The final point I’d like to highlight is the importance of offering mobile apps that “build on existing preferences of students” (Kukulska-Hulme et al., 2011, p. 32). The study indicated that communication, accessing information, and listening to music were the most intensive uses of mobile devices. It would be interesting to compare those study results with my students’ in order to have a better understanding of learning preferences specific to my learning environment and in turn integrate applicable tools to the program.
The goal of my second reading was to focus on the purpose of use of a given technology or platform. In Students’ perceptions of Facebook for academic purposes, we learn how social networking tools can support and enhance informal and social learning. One of the most relevant points in the article is the importance of perceived ease of use and perceived usefulness. For a technology to be adopted it needs to be both simple and practical; we could the same rings true of language. In addition to perceptions, it’s important to consider “how”– “how” students use a given technology and “how” the social dimension of the tool can “enhance the learning outcomes” (Sánchez et al., 2014, p. 145). In working with students who are newcomers to the area and often newcomers to the country, establishing a community that provides “support, information, friendship and acceptance” would create a “social glue” that would lead to improved learner motivation and retention (Sanchez et al., 2014, p. 141). What would social networking or learning look like on our portal? It might involve discussion boards for exchanging ideas and a featured student of the month to learn more about classmates and their backgrounds.
My final reading on Student satisfaction, learning outcomes, and cognitive loads with a mobile learning platform. touched back on the heart of my project, language acquisition, while tying together numerous relevant concepts seamlessly. My annotation after reading the abstract was “perfect” and, indeed, it certainly proved the most on-point reading of the semester. The language learning platform designed for this study bears many similarities with the project I am proposing including access to course information, instructor biographies, learning resources, student performance indicators, and discussion boards (Zhonggen et al., 2019, p. 325). The study supports the following hypotheses (Zhonggen et al.,2019, p. 232):
- Participants with the mobile learning platform are more satisfied than those without it.
- Learning outcomes improve significantly with use of the learning platform.
- Cognitive loads of participants using a mobile learning platform are significantly lower than those without it.
Hypotheses 1 and 2, while highly relevant, are reminiscent of previous studies and blog posts so I will focus on Hypothesis 3 which I found particularly worthy of further analysis. Cognitive load can be defined as “a multidimensional construct representing the load that performs a particular task imposed on the learner’s cognitive system” or a “level of information being manipulated in working memory” (Zhonggen et al, 2019, p. 328; Yu, Chen, Kong, Sun & Zheng, 2014). Cognitive load can be broken down into:
- Intrinsic cognitive load which refers to the load of the target knowledge to be acquired.
- Extrinsic cognitive load which is “subject to teaching strategies” and caused by instructional design elements (Zhonggen et al., 2019, p. 328).
- Germane cognitive load which refers to the use of working memory resources in the processing of intrinsic load.
It’s important to identify and understand cognitive load as it is a “significant indicator of the effectiveness of learning and teaching” with lower cognitive loads predicting higher learning outcomes (Zhonggen et al., 2019, p. 329). Ultimately, by reducing cognitive loads, learners “can apply cognitive strategies to EFL learning, leading to higher EFL proficiency” (Zhonggen et al., 2019, p. 336). In the design of my portal, I will have to specifically consider ways to reduce extrinsic cognitive load so that that learner’s time and energy can be reserved for “meaningful learning activities” (Zhonggen et al., 2019, p. 328). Simple language, functionality, and high ease of use are key to minimizing extrinsic cognitive load. By using the portal, which provides access to program, language, and community resources, students’ overall cognitive loads should be reduced thereby enabling them to focus on language acquisition while in the classroom. Through self-management and with the guidance of the instructor, learners’ cognitive load can be effectively managed so that an optimal learning environment is obtained and learning outcomes are reached.
Resources:
Kukulska-Hulme, A., Pettit, J., Bradley, L., Carvalho, A., Herrington, A., Kennedy, D. M., & Walker, A. (2011). Mature students using mobile devices in life and learning. International Journal of Mobile and Blended Learning, 3(1), 18–52.
Sánchez, R. A., Cortijo, V., & Javed, U. (2014). Students’ perceptions of Facebook for academic purposes. Computers & Education, 70, 138–149.
Zhonggen, Y., Ying, Z., Zhichun, Y., & Wentao, C. (2019). Student satisfaction, learning outcomes, and cognitive loads with a mobile learning platform. Computer Assisted Language Learning, 32(4), 323–341.