From Macro to Micro: Applying a Theoretical Framework for the Development of Innovative Online Instruction, Micro-Learning, and Micro-Credentialing

Abstract

With the rapid evolution of learning and instruction in the digital age, microlearning provides small, targeted blocks of information to learners to enhance knowledge retention and engagement. As the demand for eLearning increases due to Covid-19 and digitally immersed learners, microlearning allows for discrete, specialized tasks combined into customized learning plans for professional development. An online microlearning course certification with gamification incentivizes participants to earn digital badges. These digital badges are micro-credentials. These digital badges signify competency-based learning through the mastery of each unit. Microlearning courses with Merrill's First Principles of instruction maximize participant knowledge and skills by proposing to solve real-world problems. With this model, learners demonstrate that they learn the material through measurable gains in performance competencies. The literature review explores microlearning and micro-credentialing and a theoretical framework that combines connectivism, Merrill's First Principles of Instruction, and competency-based learning. With the self-paced component of microlearning, participants take ownership of their learning experience and thus, increase their motivation to learn.

Headnote

Abstract

With the rapid evolution of learning and instruction in the digital age, microlearning provides small, targeted blocks of information to learners to enhance knowledge retention and engagement. As the demand for eLearning increases due to Covid-19 and digitally immersed learners, microlearning allows for discrete, specialized tasks combined into customized learning plans for professional development. An online microlearning course certification with gamification incentivizes participants to earn digital badges. These digital badges are micro-credentials. These digital badges signify competency-based learning through the mastery of each unit. Microlearning courses with Merrill's First Principles of instruction maximize participant knowledge and skills by proposing to solve real-world problems. With this model, learners demonstrate that they learn the material through measurable gains in performance competencies. The literature review explores microlearning and micro-credentialing and a theoretical framework that combines connectivism, Merrill's First Principles of Instruction, and competency-based learning. With the self-paced component of microlearning, participants take ownership of their learning experience and thus, increase their motivation to learn.

Keywords

microlearning, online instruction certification, gamification, professional development, micro-credentials, personalized learning, competency-based learning, Merrill's First Principles of Instruction, connectivism

Introduction

With an increasing demand for virtual classes, the need for higher education instructors to go beyond traditional learning and successfully deliver online education content continues to surge. "Traditional models of instruction are often not sufficient for continuous skills update and upgrade as they are cumbersome and confine learners to prescribed and closed systems" (Fiedler & Kieslinger, 2005, as cited in Buchem & Hamelmann, 2010, p. 12). To maintain academic integrity and rigor in online programs, universities are realizing the need for targeted training and support for instructors who can cater to the new wave of digitally immersed learners. Hence, microlearning has emerged to address the need for instructors who can keep up with the advancements in education. In microlearning, content is short but has specific bursts intended to engage and inform the learner. It is a "... strategy for aligning training to the way people actually learn in order to solve meaningful business problems" (Dillon, 2018, para. 1). Significant benefits of microlearning include better concept retention, increased engagement, greater motivation, higher participation in collaborative learning, and improved learning ability and performance (Leong et al., 2021). Implementing a course consisting of discrete microlearning elements will improve instructor delivery in online education. The targeted training is modularized and scaffolded, allow for timely practice and implementation of concepts, and be customizable for various backgrounds, conditions, and learning goals. The units also are competency-based to ensure validity in certification.

Background

Microlearning

Microlearning delivers education in small units, a specific topic at a time, allowing students to learn at their own pace (Park, 2018). It consists of digital learning and related fields in learning processes in mediated environments (Park, 2018). Micro-learning is a technique that positively affects learning, produces optimistic results, and creates a friendly and comfortable environment (Park, 2018). It is a method of instruction that makes progressive outcomes in learning and skills, supports the creation of the concepts, and is helpful in learning and understanding the topics (Park, 2018). Short-term-focused strategies for skill-based instruction are an integral component of microlearning (Park, 2018). Cognitive loads for the information provided for learning micro-learning also enable finding related data, photos, and videos in mobile environments in real-time (Park, 2018).

As online education becomes an increasingly common option for learners of varying ages and interests, the demand for qualified instructors who can effectively facilitate the teaching and assessment of content in an online environment is also likely to increase. Since microlearning allows instructors to learn in small bits, the instructor trying to obtain certification demonstrates understanding based on achieving competency in small units that are less overwhelming. Some instructors have full-time jobs and families to take care of, which can be daunting to complete an entire course all at once (Avelino, 2021). In addition, microlearning for online instructional certification will allow instructors to cater to the personalized needs of learners who use digital tools, such as cell phones, to understand the world around them (Redding, 2016). Microlearning addresses the problem of virtual instruction resistance through information chunking and feasible competencies to complete. It enriches the digital learning experiences of learners with instructors well versed in digital learning.

Gamification

Gamification and microlearning motivate the learner and increase his or her engagement with the learning content by integrating the game design principles and course contents within a learning environment (Becker, 2015). Its primary focus is to engage learners by making the course materials enjoyable (Becker, 2015). The online instructor will have the opportunity to learn about different techniques to use when keeping their student's attention, so he or she wants to learn more. This way of learning increases the motivational level of online learners, which enhances the engagement and understanding of the subject material.

Problem Needing to be Solved

The issue with microlearning for instructors' online education certification is that the course is condensed, and the online instructor learns at a fast pace (Avelino, 2021). If an instructor prefers personalized guidance, the asynchronous nature of microlearning can be an impediment. Some online instructors are going back to school or are unfamiliar with the latest technology. They may lack the time or need assistance accessing their course and course assignments.

Purpose of the Literature Review

Maintaining the academic integrity of online programs to safeguard the needs of virtual learners is the purpose of this literature review. Universities need targeted training and support for higher education instructors. "Merely dividing a traditional training lesson into smaller chunks is not sufficient to promise effective microlearning" (Zhang & West, 2020, p. 315). Instructional designers must consider how each micro-lesson is sorted into personalized learning objectives and fit into a competency-based learning map. Each skill is easily categorized and referenced to ensure that learning is meaningful and adaptive to the personalized learning goals. Assessment and feedback must be available immediately so that learners can move on to the next appropriate task. The learning modality must fit learners' schedules as they see fit and not intervene with daily tasks. Online instructors require adequate training for assisting students towards learning objectives and mastering skills to achieve their broad learning goals on a virtual platform for effective online instruction. A self-paced training provides feasible, flexible, and obtainable evidence of mastery of microlearning components. Microcontent focuses only on relevant information using very brief elements and visual, interactive content (Avelino, 2021).

Literature Review

Microlearning is a form of eLearning in small units for users to acquire knowledge to achieve learning objectives and mastery of specific content (Zhang & West, 2019; Khurgin, 2015; Poulin, 2013; Singh, 2014, as cited in Major & Calandrino, 2018; Park & Kim, 2018; Fernandes, 2020). It is not highly researched but has garnered appeal through its claims of increasing knowledge retention and learner engagement (Major & Calandrino, 2018; Fernandez, 2020). The digitally immersive age and the advent of spatial distance learning coinciding with the pandemic have made eLearning the premier choice in distance learning. This stems from the flexible and self-paced online component of microlearning (Busse et al., 2020). Microlearning supports "... learning in a more easily accessible bites of information productively designed in an online environment" (Major & Calandrino, 2018, p. 1).

Designing a microlearning course for online certification requires measurable competencies, competency-based lessons, flexibility with time, assessments as evidence of mastery, differentiating support, and transparent progress monitoring that is easily accessible to the users (Center on Innovations in Learning, 2018). "Research shows that providing e-learning training in small units has benefits for employees and companies" (Fernandes, 2020, p. 4). Studies demonstrate potential in microlearning for professional development through personalized learning. Microlearning leverages self-paced coursework founded on competencybased mastery digital badges to encourage the learning experience (Gamrat et al., 2014). Research states that microlearning provides flexibility with self-paced online content and learning availability for virtual learners. During the COVID-19 pandemic, eLearning fulfilled learner needs in a rapidly changing digital era (Busse et al., 2020; Zhang & West, 2019; Park & Kim, 2018; Center for Innovations in Learning, 2018; Fernandes, 2020). The demand for microlearning has increased as companies lean towards online learning solutions for professional development. Other forms of professional development such as trainer led events or hour-long online courses are comparatively ineffective. "Only 15% of employees can effectively apply new knowledge and up to 80% forget such knowledge within a month (Diaz Redondo et al., 2020). Microlearning can help improve levels of engagement and build retention. Its flexibility also allows employees to complete learning objectives in their own time. It is cost-efficient to produce and reassign into other forms of training. Microlearning can take on many forms, such as gamification. Gamification strategies in microlearning enrich the learner's experience by utilizing technology to increase requisite knowledge and motivation toward learning milestones. Gamification, coupled with cognitive models, organize the educational content, and guide the learner to achieve their learning objectives (Diaz Redondo et al., 2020).

Review of the Historical Literature

In the rapidly developing modern world, it is necessary to be able to meet the challenges of the current times (Khaitova, 2021). The transition to a competency-based approach sets the educational system the task of using modern educational technologies to form required competencies (Khaitova, 2021). The first study of gamification began with video-game-based learning, which set out to prove students can learn competencies through video game interactions (Kaitova, 2021). The second study that followed developed the "Serious Games" movement known as gamification today (Kaitova, 2021). "The Serious Games program was created in 2002 by Ben Sawyer and David Reetsky and brought together private, academic, and military communities who used fully functional games to teach and create various nongame simulations" (Khaitova, 2021). In 2003, Nick Pelling coined gamification to bring playful elements to computer hardware. Later, in 2005, gamification was the first platform to include game mechanics, such as scoring, to improve business productivity (Khaitova, 2021).

The interest in gamification of learning peaked at almost the same time as microlearning emerged in 2004 (Javorcik & Polasek, 2019). Its gradual development has gone hand in hand with the advancement of various technologies. From sending textbooks via email to adaptive Learning Management Systems (LMS) and integrating artificial intelligence, eLearning has continued to evolve (Egerová, 2011, as cited in, Javorcik & Polasek, 2019). With the availability of new technology, eLearning, microlearning, and gamification has evolved.

Review of the Current Literature

Current literature postulates that microlearning facilitates the digitization of learning units into small components. This approach embraces the evolving digital age that caters to learners who are immediate consumers of information (Fernandes, 2020; Busse et al., 2020; Park & Kim, 2018; Major & Calandrino, 2018). Studies have indicated that microlearning is still in its inception but shows promise. Findings demonstrate improved knowledge retention and learner engagement with short competency-based learning units (Major & Calandrino, 2018; Zhang & West, 2019). Research has tried developing microlearning for professional development by employing its self-paced aspect and a standalone approach to learning without a trainer. As a result, microlearning achieves time and cost-efficiency (Fernandes, 2020).

Digital badges and the flexibility to be self-paced, as well as being device agnostic, allow microlearning a degree of personalized learning. This aspect of microlearning is present, even for online instruction certification (Gamrat et al., 2014; Fernandes, 2020). These digital badges are micro-credentials that learners acquire to demonstrate content mastery per unit. Upon completion, earned badges become an online instruction certification (Center on Innovations in Learning, 2018). Micro-credentials with evidence of content mastery are in the process of professional development credit recognition. A notable example is Arkansas' approval through Act 745, and "... Arkansas is currently networking with other states for universal recognition" (Center on Innovations in Learning, 2018, n. p.).

Furthermore, a microlearning course provides the eLearning option for virtual participants due to the coronavirus (Busse et al., 2020). Research indicates that developing microlearning makes content development more efficient by replacing large amounts of static learning material with simplified production of content for learners (Park & Kim, 2018). This allows essential design elements in microlearning for learning and evidence of content mastery (Zhang & West, 2019; Center on Innovations in Learning, 2018).

Organizations are shifting their focus from in-person workshops to incorporating more eLearning opportunities for their employees (Emerson & Berge, 2018). As technology integrates into employees' daily tasks, more blended approaches to training and development are shifting into learning in the workplace (Emerson & Berge, 2018). Companies continue to look for training solutions that involve learning management systems, shareable platforms, and independent consumption by employees. By implementing microlearning strategies, companies can train employees by delivering discrete chunks of information in short bursts of time (Emerson & Berge, 2018). Using microlearning segments is a strategy that complements more comprehensive classroom and web-based training by reinforcing concepts between tasks (Emerson & Berge, 2018). Microlearning metrics evaluate the degree of mastery per unit. Examples such as pre-tests and post-tests provide evidence for these metrics (Center on Innovations in Learning, 2018; Busse et al., 2018). Earning micro-credentials is a matter of successful completion of each unit and therefore fulfilling the respective learning objective (Busse et al., 2020). What determines the successful implementation of microlearning is participants acquiring the end goal, such as the online instruction certification in this case, which serves as proof of mastery of content through skill-based learning (Zhang & West, 2019).

Theoretical Framework

Theoretically, this work fits within a framework of three theories. Connectivism provides a conceptual theory of references for how to achieve learning. Merrill's First Principles of Instruction, which provides a framework for instruction and the design of excellent instruction, regardless of the mode of delivery, and competency-based learning help to give perspective to what is valuable, not only to education but also to the marketplace. Certifications are, more often than not, competency-based learning. Micro-credentialing extends competency to allow for a personalized learning approach (Gamrat et al., 2014; Zhang & West, 2019; Center on Innovations in Learning, 2018). The International Board Standards for Training, Performance, and Instruction (IBSTPI) provide a comprehensive set of standards. These standards apply to the instructional design process for online learning and online learner and instructor competencies.

Connectivism as a Launchpad

In 2004, George Siemens presented the theory that students learn best when instructed on using social networking to improve their understanding of the pathways to learning (Siemens, 2004). In the connectivist realm, a student also can be an expert contributing a diverse set of ideas, relationships, and transformative truths that can emerge from each person inserting themselves into both sides of the learning equation at the same time. A student learning Botany is instructing peers on building a birdhouse on YouTube. Siemens saw this as something that ultimately could be beneficial to all learning environments, especially digital ones where a massive number of ideas coalesced together in a highly transformative way (McAuley, Stewart, Siemens, & Cormier, 2010). Thus, the first Massive Open Online Course (MOOC) was born. His idea was to promote the idea that communities can help solve problems. The larger the community, the better versed they were to solve problems (Greenwood, 2020). Siemens saw that the roles of educators and designers could radically shift (Siemens, 2008). These shifts would place more value on ideas like microlearning that allow a student to be more ubiquitous and selfpaced. Instructors also could provide resources and guidance with greater flexibility. This launchpad is for microlearning and micro-credentialing to move students successfully toward aggregation of knowledge and development of skills. This need is satisfied as industries continue to shift toward a more competency-based approach to learning and development. This kind of competency-based learning is the norm for most industries and is critical to ensuring the skills and knowledge necessary to drive the sustainability of current career paths. Those innovations of new career paths remain fruitful as marketplaces shift and technology continues to drive innovation at a rapid clip.

Merrill's First Principles of Instruction

Within Merrill's First Principles of Instruction, learning results when learners engage in solving real-world problems (Merrill, 2020). Integrating microlearning into professional development allows learners to apply their skills to real-world specific tasks. In Merrill's framework, direct learners recall, relate, describe, and apply knowledge from relevant past experiences as a foundation for newly acquired knowledge (Merrill, 2020). Microlearning determines what learners already know and creates a learning pathway that commensurate with their experience. They can review knowledge in small segments, rather than revisiting an entire course.

According to Merrill, the most effective learning products or environments are problemcentered with four distinct phases of learning: activation of prior experience, demonstration of skills, application of skills, and integration of these skills into real-world activities (Merrill, 2020). Implementing this approach to developing a microlearning course will motivate learners to complete their learning objectives. Learners will integrate instruction into their lives when they can demonstrate improvement in skill, defend their new knowledge, and modify their new knowledge for use in their everyday lives (Merrill, 2020).

Adopting Merrill's Principles of Instruction into microlearning provides learner guidance as follows: direction to pertinent information, multiple representations for demonstrations, and relevance for modeling behavior (Merrill, 2020). Microlearning allows the learner to learn through demonstrations through diverse forms of content used immediately in his or her work environment. According to Merrill's Principles of Instruction, learning thrives when learners solve varied problems (Merrill, 2020). Microlearning allows learners to achieve learning goals by accomplishing a specific task before continuing to another skill. Furthermore, in line with Merrill's Principles of Instruction, learning occurs when learners integrate the new knowledge or skill into their everyday life (Merrill, 2020). Microlearning units allow learners to employ new knowledge instantly in a short period (Khaitova, 2021).

Competency-Based Learning

Competency-based learning utilizes predetermined competencies to develop instruction that users need to complete successfully to demonstrate content mastery (Zhang & West, 2019). Each mastery of these competency-based learning units becomes micro-credentials, serving as evidence of short competency-based learning units' mastery for acquiring the online instruction certification (Center on Innovations in Learning, 2018). Microlearning delivers detailed information that focuses on a specific topic and does not consume much detail (Volz, 2020). With microlearning, the online instructor can learn a small amount of information at a time in a competency-based course (Volz, 2020). Professional development for instructors within microlearning is using the framework of competency-based education. Training entails predetermined competencies from which instruction develops and focuses on individual learning goals (Zhang & West, 2020). For learning and development competencies, the International Board Standards for Training, Performance, and Instruction (IBSTPI) describes a comprehensive set of standards that speak to the instructional design process for online learning. It also provides learning standards for online learner and instructor competencies.

Micro-credentials and Personalized Learning

A way to represent these micro-credentials is through online badges to personalize learning (Gamrat et al., 2014). Personalized learning "... allow micro-credential issuers, or earners, to organize micro-credentials into personalized paths where earners select their preferred option for each requirement on the path" (Zhang & West, 2019). A system of badge pathways, where the users pick their path of mastery in the online course of instruction, allows a personalized learning touch. To develop personalized competencies, instructional designers determine several goals and skillsets for smaller micro-lessons that focus on only one specific skill (Buchem & Hamelmann, 2010). Personalized competency goals and skillsets promote knowledge retention and user motivation to learn in feasible small steps (Zhang & West, 2019).

In light of the literature, research gaps have surfaced because of the limited researchbased literature on microlearning (Zhang & West, 2019). Microlearning is still in its early development, and a broad range of studies would make substantial research contributions to microlearning effectiveness (Zhang & West, 2019). With this limitation and uncertainty of how digital learning will evolve for instruction, there is no telling what the role of micro-content will be in the future (Busse et al., 2020). These claims are research gaps concerning changes to future eLearning and its effects on the content development methods of microlearning (Park & Kim, 2018).

For professional development, determining what measurable competencies to use, rather than developing new measurable competencies for micro-credentials in competency-based learning, is left out of research (Center on Innovations in Learning, 2018). Micro-credentials in the form of a digital badging system also have some research restrictions with the need for "...additional research in the areas of the design of digital badging systems and their applications to learning in professional contexts" (Gamrat et al., 2014, p. 1147). Further research gaps include "...further research and practice including design and development of microlearning lessons for professional development" (Zhang & West, 2019, n. p.). Furthermore, research provides quantitative and qualitative evidence of how effective microlearning is for professional development (Zhang & West, 2019).

Additionally, there is insufficient research on microlearning as an effective strategy for reaching long-term learning goals (Javorcik & Polasek, 2019). There is little evidence of the perceived benefit of utilizing microlearning for instructional purposes. With too many learning designers, microlearning does not have a set of fixed criteria, thus making it more difficult to define. Some learning designers define microlearning based on duration, such as two- to sevenminute lessons (Kapp & Defelice, 2018). Still, others assert that microlearning is objective-based and only focused on completing one competency at a time with little consideration for the duration (Kapp & Defelice, 2018).

The benefits of using microlearning for long-term or complex skill development are limited in scope. "For long-term learning goals, microlearning interventions could end up as content fragments that are not tied together" (Javorcik & Polasek, 2019, p. 256). Learners may complete competencies by their learning needs and preferences. This approach does not guarantee that learners will perceive the intended macro level of learning. Furthermore, "we cannot be certain that learners will synthesize content from microlearning well enough to construct appropriate mental models" (Javorcik & Polasek, 2019, p. 256). Engaging learners in microlearning does not guarantee that they will create the correct mental models that are essential to apply in real-world situations and improve productivity. Learning designers must create clear pathways for completing learning goals and objectives and allow learners to revisit concepts to promote long-term learning.

Summary

To better understand how they impact knowledge retention, learner engagement, flexibility, and cost-efficiency, there needs to be further research about microlearning and microcredentialing. Contemporary learners often are immediate consumers of information. Instructional designers and developers engage in further research about the design and delivery of microlearning courses for online instruction and certification. They also gather data on the effects of microlearning for professional development and use in higher education. Microcredentialing, such as digital badges, provides pathways for instructional designers, program directors, and other educational professionals. These digital badges create student success and learning opportunities by ensuring that competencies and skills are adaptable and valuable to the marketplace.

Sidebar

About the Authors

Kirenia Maytin is an Education Technology Specialist who implements educational technology in hybrid learning settings. She is a former graduate student at St. Thomas University who studied Instructional Design and Technology. She has a master's degree in Curriculum and Instruction and a specialist degree in Educational Leadership and Policy. She has eight years of educational experience working in teaching, assessment, and technology. Her research interests include virtual instructional design, educational technology innovations, and professional development training.

Felicia Fulmore is a Head Start teacher in Miami, Florida. She received her master's degree at St. Thomas University in Instructional Design and Technology. Felicia also has a bachelor's degree in social work from Florida International University. She has four years of experience working with toddlers 3- to 5-year-olds. She is new to the education profession and plans to receive her teaching certification in PreK to 3rd and Biology 6th to 12th grade.

Yanira Leon is an Instructional Designer at Florida International University. She is a graduate of St. Thomas University, majoring in Instructional Design and Technology. She has a master's degree from Texas State University in Business Administration. She has eight years of experience in education as a classroom teacher for K-12 and as an instructor for higher education. Her research interests are instructional design for online learning, accessibility for online learners, gamification, and microlearning.

Angela McCullough is a former graduate student at St. Thomas University who majored in Instructional Design and Technology. She received her bachelor's degree in Fine Arts in Graphic Design from Sam Houston State University. She has 15 years of teaching experience, the majority in elementary education. Over her career, she has taught 3rd, 4th, and 5th-grade math and science, and middle and elementary school art.

Dorothy Ragus is Director of Curriculum and Content Development at Logan University, with 25 years of experience in curriculum and training development, assessment, and administration. She has managed the development of Interactive Multimedia Instruction and Programs of Instruction for the DoD and created professional development programs for global DoDEA K-12 educators. Her research interests include curriculum planning and assessment and the correlation of course development processes with instructor engagement.

Timothy M. Stafford is the Program Director-MS in Instructional Design & Technology, Dissertation Chair, and Online Associate Professor at St. Thomas University. He has more than 30 years as an educational leader and innovator with an emphasis on leading educational technology and e-learning development teams for blended and asynchronous learning environments, institutional technology rollouts, accreditation, curricula development, and development of professional learning environments and infrastructures. He has overseen the development of professional learning systems for national trade associations including the American Institute for Architects and the National Society of Professional Engineers. His research interests are in E-learning systems, Delphi studies and their use in educational metrics, Connectivism, instructional design for online learning, educational technology, learning forensics, digital literacy, and the connection of epistemology and learning.

Acknowledgement

The authors acknowledge the contribution of Christian O. Maytin, who edited this article.

Discussion Questions

1. What are some of the benefits that learners experience with microlearning for instruction?

2. Why would companies use microlearning for competency-based training? What are some of the disadvantages of traditional forms of instruction?

3. According to Merrill's Principles of Instruction, how does microlearning promote learners to integrate new knowledge from professional development?

4. What are some of the limitations to implementing microlearning?

5. Does microlearning or macro-level learning benefit learners long-term?

To Cite this Article

Maytin, K., Fulmore, F., Leon, Y., McCullough, A., Ragus, D., & Stafford, T. M. (2023, Spring). From macro to micro: Applying a theoretical framework for the development of innovative online instruction, micro-learning, and micro-credentialing. Journal of Multidisciplinary Research, 75(1), 161-172.

References

References

Avelino, J. (2021, October 20). 10 microlearning challenges: EdApp microlearning. EdApp Microlearning Blog. https://www.edapp.com/blog-10-microlearning-challenges/

Becker, K. (2015). Gamification: How to gamify learning and instruction. 2015 IEEE Games Entertainment Media Conference (GEM). https://doi.org/10.1109/gem.2015.7377207

Buchem, I., & Hamelmann, H. (2010). Microlearning: A strategy for ongoing professional development. eLearning Papers, 21(7), 1-15.

Busse, J., Lange, A., Hobert, S., & Schumann, M. (2020). How to design learning applications that support learners in their moment of need - Didactic requirements of micro learning. AMCiS 2020 Proceedings, 15. https://aisel.aisnet.org/amcis2020/is_education/is_education/15

Center on Innovations in Learning. (2018, February 13). Micro-credentials for competencybased learning. Center on Innovations in Learning. http://www.centeril.org/reports/resources/Solution-Finding%20Report%20-%20Microcredentials%20for%20Competency-Based%20Learning%202-20.pdf

Diaz Redondo, R. P., Ktena, A., Kunicina, N., Zabasta, A., Patlins, A., & Mele, D. E. (2020). Advanced practices: Micro learning, practice oriented teaching and gamified learning. 2020 IEEE 61st International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 1-7, https://doi.org/10.n09/rtucon5n74.2020.9316555

Dillon, J. d. (2018, May). A microlearning experience about Microlearning. Training. https://trainingmag.com/a-microlearning-experience-about-microlearning/

Emerson, L. C., & Berge, Z. L. (2018). Microlearning: Knowledge management applications and competency-based training in the workplace. Knowledge Management & E-Learning, 10(2), 125-132.

Fernandez, I. S. (2020, November). How to develop microlearning. HR.COM, 1-5.

Gamrat, C., Zimmerman, H. T., Dudek, J., & Peck, K. (2014). Personalized workplace learning: An exploratory study on digital badging within a teacher professional development program. British Journal of Educational Technology, 45(6), 1136-1148. https://doi.org/10.1111/bjet.12200

Javorcik, T., & Polasek, R. (2019). Practical application of microlearning in education of future teachers. Proceedings of the 18th European Conference on e-Learning, 254-259. https://doi.org/10.34190/eel.19.031

Kapp, K., & Defelice, R. (2018, July). Elephant sized impact. TD: Talent Development, 72(7), 26-30.

Khaitova, N. F. (2021). History of gamification and its role in the educational process. International Journal of Multicultural and Multireligious Understanding, 8(5), 212-216.

Leong, K., Sung, A., Au, D., & Blanchard, C. (2021). A review of the trend of microlearning. Journal of Work-Applied Management, 13(1), 88-102. 10-2020-0044

Major, A. E., & Calandrino, (2018). Beyond chunking: Micro-learning secrets for effective online design. FDLA Journal, 15(2), 1-5.

McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010). The MOOC model for digital practice. http://www.elearnspace.org/Articles/MOOC_Final.pdf

Merrill, M. D. (2020). First principles of instruction (rev. ed.). Association for Educational Communications & Technology.

Park, Y., & Kim, Y. (2018). A design and development of micro-learning content in e-learning system. International Journal on Advanced Science, Engineering, and Information Technology, 8(1), 56. https://doi.org/10.18517/ijaseit.8.1.2698

Redding, S. (2016). Competencies and personalized learning. In M. Murphy, S. Redding, & J. Twyman (Eds.), Handbook on personalized learning for states, districts, and schools (pp. 3-18). Philadelphia, PA: Temple University, Center on Innovations in Learning. https://compcenternetwork.org/sites/default/files/archive/Redding_chapter_web.pdf

Siemens, G. (2004). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2, 3-10.

Siemens, G. (2008). Learning and knowing in networks: Changing roles for educators and designers. University of Georgia IT Forum, Paper 105. http://it.coe.uga.edu/itforum/Paper105/Siemens.pdf

Volz, V. (2020, January 29). Nine ways to use microlearning in your teaching. OpenStax. https://openstax.org/blog/9-ways-microlearning-your-teaching

Zhang, J., & West, R. E. (2020). Designing microlearning instruction for professional development through a competency-based approach. TechTrends, 64(2), 310-318.