Adults experiencing situational learning barriers (such as lack of employer support for learning, difficult work schedules, and/or family responsibilities) (Roosmaa & Saar, 2017) should add innovative mobile learning technologies to their digital learning hubs. Through the ubiquitous availability of mobile devices, adults increasingly engage in mobile learning (M-learning) using smartphones and small-screen devices. M-learning enables them to address situational learning barriers by accessing information independently of time and space and managing their own learning processes based on their individual differences (Talan, 2020). Through location-aware mobile devices, learning opportunities are immediately available and personalized to learners’ interests and needs (Bruck et al., 2012). They place students at the center of the teaching and learning process by prioritizing individual differences and helping learners analyze and synthesize information (Talan, 2020). With the widespread use of mobile apps on small-screen devices, new mobile learning opportunities are increasingly available to adults seeking to overcome situational barriers.
The Embrace of M-learning
The M-learning apps on mobile computing devices present an exciting new frontier in adult education. They offer flexibility, self-paced learning, access to global knowledge pools, and personalized content to deliver a unique learner-centered education (Talan, 2020). Students’ learning can take place anywhere throughout the day; and focus on work demands, self-improvement, or leisure (Wang et al., 2019). However, adults need access to these technologies to engage in M-learning.
Effective M-learning requires three essential elements. First, adults must have access to mobile learning devices (such as small, portable devices equipped with wireless communication abilities, strong computational power, and context-aware tools) (Wang et al., 2019). In 2021, most American adults owned these devices: 85 percent owned smartphones, and 50 percent owned tablets (Pew Research Mobile Factsheet, 2021). Second, adults need access to an appropriate communications infrastructure (such as the Internet) that can connect their mobile computing devices to relevant learning materials and/or other learners (Wang et al., 2019). About 93 percent of American adults use the Internet, and 75% have broadband Internet service at home (Pew Broadband Research, 2021). Third, adults should have access to learning activities, in traditional classrooms, outside the classroom, or in informal learning contexts (Wang et al., 2019). Through ownership of mobile devices and access to the Internet, most American adults can pursue learning activities through mobile learning apps.
Web2.5 Mobile Learning Apps
Historically, mobile apps are Web2.0 tools available since the early 1990s. These legacy apps were like traditional radio stations that broadcasted the same songs or shows to all listeners without personalization. There was no real interaction; everyone received the same information simultaneously and in the same sequence. Similarly, adults could be frustrated using Web2.0 mobile apps. They only allowed learners to receive the same content in the same format and sequence, regardless of their individual learning preferences and needs.
Many M-learning apps are now more appealing to busy adults. They are successfully integrating elements of Web3.0 technologies that allow adults to gain online access to interactive learning materials, micro-learning, personalization, simulations, voice and image recognition capabilities, and rich educational games (Damyanov & Tsankov, 2018). These Web2.5-oriented apps are like a streaming service resembling Spotify that provides more highly personalized experiences. Through AI, they understand learner needs and preferences and recommend more personalized, efficient, and engaging learning experiences based on each person’s behavior, preferences, and performance. These empowering capacities have also gained the attention of established educational organizations, as mobile apps are now essential in educational courses (Talan, 2020). Consequently, the worldwide market for M-learning apps is expected to grow from $7.98 billion U.S. dollars in 2015 to $325 billion by 2025 (Wang et al., 2019). This explosive growth has produced a dizzying array of desktop-first and mobile-first apps available to assist adults in learning a large quantity and variety of content.
Desktop-First Mobile Apps. One way to make sense of mobile apps' confusing number and variety is to categorize them by the devices for which they were originally designed. For example, some desktop-first apps were designed specifically for desktop and laptop computers, allowing learners more extensive, in-depth, and longer learning sessions. Learners can use a mouse, keyboard, and large computer screens to engage a broad range of content for 30 minutes or more. Consequently, most mobile versions of desktop-first apps tend to be retrofitted into more compact mobile designs.
Desktop-first mobile apps are often available from a broad range of credit (and non-credit) courses in established education and training organizations supported by Learning Management Systems (such as Blackboard, Canvas, and Moodle). Similarly, these apps are available from digital learning platforms that support many general courses (such as Coursera, Udemy, LinkedIn Learning); literacy courses (such as Learning Upgrade); and language learning courses (such as Rosetta Stone). Many of these are fee-based platforms and typically require enrollment to access their mobile apps. Other platforms, including Khan Academy, TED Talks, and Sololearn, provide free mobile learning apps on various topics and assist learners in developing a wide range of knowledge and skills. These desktop-first mobile apps tend to have more limited functionality than desktop applications; however, they often have Web2.5 features (such as personalization and adaptive learning). A fuller range of Web2.5 mobile learning features is generally more apparent in mobile-first learning apps.
Mobile-First Learning Apps. Mobile-first learning apps (such as Babbel and Duolingo ) have been specifically designed for small-screen mobile learning applications but often provide access via desktop and laptop computers. They ensure that the apps are optimized for mobile devices through responsive designs, simplified layouts, and mobile-friendly interactions. Device-specific interactions and capabilities like touch gestures, sensors, and push notifications ensure that mobile-first app users can initiate swipe gestures for navigation, utilize the device camera for augmented reality features, or use push notifications for reminders about upcoming lessons or assignments. Also, location-based features can provide localized learning experiences. This can include location-specific content or recommendations based on the learner's location. Many of these features are integrated into the Duolingo Mobile app.
The Duolingo Mobile-First Learning App. The Duolingo mobile-first language learning app illustrates how using Web2.5 technologies can empower learners. It operates under a freemium business model and provides the core services of the app free of charge. Yet, it seamlessly integrates personalization, gamification, and adaptive learning experiences based on a user’s individual abilities, preferences, progress, and learning style (Viktor, 2021; Marr, 2020). Through smart learning environments, the platform uses several innovative approaches to keep learners updated and engaged on the app:
· Intelligent tutoring systems enhance content based on learners' needs, strengths, and weaknesses (Marr, 2020). They provide helpful, personalized feedback, hints, and guidance during learning.
· Mobile-Based microlearning units optimize the duration, focus, and benefits of learning into bite-sized learning modules, lessons, and short quizzes, that can be completed in a few minutes (Moore et al., 2024).
· Spaced repetitions optimize learners’ memories of targeted content by providing scheduled reviews of personalized language lessons over longer intervals (Marr, 2020; Tabibian et al., 2019).
· Stealth assessment reduces learner anxieties by intentionally blurring the distinction between assessment and learning so that the learner is oblivious to the assessment process (Georgiadis et al., 2020).
Notwithstanding these innovative interventions, the Duolingo mobile app does not fully utilize the depth of Web3.0 technologies.
Web3.0 Mobile Apps
Fully Web3.0 mobile apps are scarce but still emerging. Nevertheless, one example is the BitDegree platform, which provides a mobile app for learning and exploring knowledge about blockchain technology. The platform offers many courses on cryptocurrencies and provides numerous educational materials to assist learners interested in digital currencies and blockchain technology. It utilizes blockchain tools (such as tokens and certificates) to track learning achievements transparently, award scholarships, and provide incentives.
Selecting M-learning Apps
As you consider mobile apps for adoption into your digital learning hub, you should ponder the extent to which you have access to appropriate M-learning tools and the types of apps suitable for your learning needs. Top of mind should be desktop-first and mobile-first Web2.5 learning apps capable of providing relevant learning experiences. Fully Web3.0 mobile apps are still in the development stage. You should be mindful of adopting developmental technology.
Up Next: Immersive Learning in Augmented and Virtual Reality Environments
In my next blog post, I analyze the key features of AR
and VR environments and the tools that should be considered for adult learners’
digital tool kits.
Larry
G. Martin, Ph.D.
Professor
Emeritus, UWM
Follow me on X
(formerly twitter) https://twitter.com/larry_martin29 and LinkedIn https://www.linkedin.com/in/larry-martin-142b528/
References
Bruck, P. A., Motiwalla, L., & Foerster, F. (2012). Mobile learning with micro-content: a framework and evaluation.
Damyanov, I., & Tsankov, N. (2018). Mobile apps in daily learning activities. iJIM, 12(6).
Georgiadis, K., van Lankveld, G., Bahreini, K., & Westera, W. (2020). On the robustness of stealth assessment. IEEE Transactions on Games, 13(2), 180-192.
Marr, B. (2020) The Amazing Ways Duolingo Is Using Artificial Intelligence To Deliver Free Language Learning. https://www.forbes.com/sites/bernardmarr/2020/10/16/the-amazing-ways-duolingo-is-using-artificial-intelligence-to-deliver-free-language-learning/?sh=16c276275511
Moore, R. L., Hwang, W., & Moses, J. D. (2024). A systematic review of mobile-based microlearning in adult learner contexts. Educational Technology & Society, 27(1), 137-146.
Talan, T. (2020). The effect of mobile learning on learning performance: A meta-analysis study. Educational Sciences: Theory and Practice, 20(1), 79-103.
Roosmaa, E. L., & Saar, E. (2017). Adults who do not want to participate in learning: A cross-national European analysis of their perceived barriers. International Journal of Lifelong Education, 36(3), 254-277.
Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., & Gomez-Rodriguez, M. (2019). Enhancing human learning via spaced repetition optimization. Proceedings of the National Academy of Sciences, 116(10), 3988-3993.
Viktor (2021). The Duolingo Business Model – How Does Duolingo Make Money? https://productmint.com/duolingo-business-model-how-does-duolingo-make-money/
Wang, Y. Y., Wang, Y. S., Lin, H. H., & Tsai, T. H. (2019). Developing and validating a model for assessing paid mobile learning app success. Interactive learning environments, 27(4), 458-477.