mobile learning

Benefits of Mobile Learning Programs for Your Employees

Employee training is a non-negotiable factor in reaching your business goals. An organization cannot grow if its workers aren't growing themselves – and there's no better stimulus for professional development than workplace education. Today's difficult economy motivates many enterprises to cut down on employee training expenses, but they're clearly unaware of the value of employee education to the health of an organization.

The National Center of the Educational Quality of the Workforce reveled in a recent study that a 10% increase in workforce education level results in an 8.6% percent gain in total productivity. (http://www.businessknowhow.com/manage/higherprod.htm) This statistic proves that employee productivity is directly connected to the amount of training they receive. Employers who want to grow and efficiently operate their businesses simply must invest in employee training.

Fortunately, today's ever-present mobile devices offer many interesting opportunities for flexible and cost-effective worker education. Here are some key benefits of mobile learning for your employees and organization.

Employees find it easy to learn on mobile

To put it simply, employees like to use their mobile devices and once they see that learning can be as easy as glancing at a smartphone or tablet in a free minute, they'll be motivated to do it on a regular basis. Mobile learning allows learners to access content from any device and any corner of the world. Some mobile learning apps work in offline, so access to the internet is not even an issue. Mobile learning provides a great user experience and it's definitely user-friendly.

Mobile learning delivers key data whenever needed

It's clear that mobile devices are constantly on, connected to the web and within easy reach of employees, helping them to access relevant information at any time. Mobile learning is great for just-in-time (JIT) training – it can be refresher modules on product specifications, pricing details, and other kinds of time-sensitive information. By having all this information at their fingertips, employees can easily boost their performance, improving their decision making processes and ensuring better customer satisfaction. Additionally, mobile learning empowers people – just as stated in the 2012 report Mobile Learning: Driving Business Results by Empowering Employees in the Moment: "Putting learning in the palm of people’s hands — exactly what they need, when they need it — can have an immediate positive effect on the bottom line". (http://www.slashdocs.com/mukrvy/mobile-learning-driving-business-results-by-empowering-employees-in-the-moment.html)

Mobile devices can quickly distribute learning

The mobile age brings greater mobility of your staff and this impacts the ways in which enterprises train their employees. Investing in training opportunities onsite is rapidly diminishing. Many employees frequently travel or work on the move – this means that they spend lots of time without access to laptops or desktop computers. mLearning is a great solution here because it allows companies to easily spread learning materials to employees, full of practical knowledge about many areas of business. The power of mobile learning lies in the fact that it connects employees to all the knowledge and expertise they need, exactly when and where they need it. It addresses any potential learning need at any time.

Mobile learning is flexible

This is a key benefit brought by mobile learning. Flexibility offered by mobile learning solutions can be interpreted in different ways. First, there's the flexibility of time and space, where workers can choose the location and time of learning themselves. Moreover, they're also free to choose the device for their learning, as long as it can correctly display learning materials. The learning itself is also more flexible because it can integrate a wide variety of formats, including podcasts or videos.

Mobile learning helps to save time

This kind of learning will fit into the busiest schedules. It requires less time than instructor-led training or long eLearning programs. Instead of taking an entire course, learners can access training modules themselves to learn exactly what they need to know. This reduces the time which needs to be dedicated to training, minimizing productivity losses. Employees should be equipped with appropriate skills and knowledge as quickly as possible – and this is something that only mLearning can offer. Learners can consume small amounts of content every time, and study it whenever they like.

Improved completion rates and higher retention

Already in 2007, the Mobile Learning and Student Retention Report showed that mLearning brings higher retention rates. (http://files.eric.ed.gov/fulltext/EJ800952.pdf) And no wonder – with its bite-sized or micro-learning approach, mLearning offers a learning environment which makes it easier for learners to initiate learning and motivates them to complete it, fostering their knowledge retention.

Mobile performance support

Today, mobile learning is recognized as a beneficial approach for providing performance support intervention. It's safe to say that mobile devices are a part of every employee's work environment. Delivering performance support solutions directly into their mobile devices, employers are facilitating easy access to information while at work and improving the probability of its usage and retrieval.

Higher engagement in mobile

The 2010-2011 Horizon Report has already shown us the value of mobile learning in fostering learner engagement with the learning materials. (http://www.nmc.org/pdf/2011-Horizon-Report.pdf) Mobile learning experiences are more immersive and countless statistics reveal that a higher number of learners complete courses through mLearning than through traditional training or even cutting-edge eLearning solutions.

Well-defined learning path

Mobile devices offer an excellent measure to help learners see and update their learning path, showing learning as a continuous process. Many employees organize their lives through their mobile devices and by integrating links to these apps, mLearning solutions help learners to save time and accurately plan their learning. This is also relevant to alleviating the impact of the so-called Forgetting Curve which defines the exponential nature of forgetting. According to experts, we tend to forget 80% of what we've learned during the last 30 days. A short training period once a year cannot be expected to hold real impact over employee performance for a long time. Having regular access to a variety of mLearning materials works against the Forgetting Curve and helps employees to make the most from the learning opportunity.

Mobile learning is a solution which brings lots of benefits to companies that decide to invest in learning programs organized on mobile devices. It improves knowledge retention rates, boosts learners' engagement with materials, empowers employees to develop new job skills and appeals to all those talents who are constantly looking for non-traditional learning opportunities to help them grow. mLearning creates a swift learning process which is bound to positively affect employee productivity at an enterprise.

Guest Blog Contributor By-line:
Carol Williams is a team member at Honeybells - a fruit shipping firm from Florida. She has an intense background in mLearning which she combines with her passion for anything tech and mobile related.





mobile learning

Self-regulated Mobile Learning and Assessment: An Evaluation of Assessment Interfaces




mobile learning

Making Mobile Learning Work: Student Perceptions and Implementation Factors

Mobile devices are the constant companions of technology users of all ages. Studies show, however, that making calls is a minimal part of our engagement with today’s smart phones and that even texting has fallen off, leaving web browsing, gaming, and social media as top uses. A cross-disciplinary group of faculty at our university came together in the mLearning Scholars group to study the potential for using mobile devices for student learning. The group met bi-weekly throughout a semester and shared thoughts, ideas, resources, and examples, while experimenting with mobile learning activities in individual classes. This paper summarizes student perceptions and adoption intent for using mobile devices for learning, and discusses implementation issues for faculty in adding mobile learning to a college course. Outcomes reflect that mobile learning adoption is not a given, and students need help in using and understanding the value in using personal devices for learning activities.




mobile learning

Mobile Learning, Cognitive Architecture and the Study of Literature




mobile learning

Towards a Method for Mobile Learning Design




mobile learning

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.




mobile learning

A Model Predicting Student Engagement and Intention with Mobile Learning Management Systems

Aim/Purpose: The aim of this study is to develop and evaluate a comprehensive model that predicts students’ engagement with and intent to continue using mobile-Learning Management Systems (m-LMS). Background: m-LMS are increasingly popular tools for delivering course content in higher education. Understanding the factors that affect student engagement and continuance intention can help educational institutions to develop more effective and user-friendly m-LMS platforms. Methodology: Participants with prior experience with m-LMS were employed to develop and evaluate the proposed model that draws on the Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and other related models. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to evaluate the model. Contribution: The study provides a comprehensive model that takes into account a variety of factors affecting engagement and continuance intention and has a strong predictive capability. Findings: The results of the study provide evidence for the strong predictive capability of the proposed model and supports previous research. The model identifies perceived usefulness, perceived ease of use, interactivity, compatibility, enjoyment, and social influence as factors that significantly influence student engagement and continuance intention. Recommendations for Practitioners: The findings of this study can help educational institutions to effectively meet the needs of students for interactive, effective, and user-friendly m-LMS platforms. Recommendation for Researchers: This study highlights the importance of understanding the antecedents of students’ engagement with m-LMS. Future research should be conducted to test the proposed model in different contexts and with different populations to further validate its applicability. Impact on Society: The engagement model can help educational institutions to understand how to improve student engagement and continuance intention with m-LMS, ultimately leading to more effective and efficient mobile learning. Future Research: Additional research should be conducted to test the proposed model in different contexts and with different populations to further validate its applicability.




mobile learning

Continued Usage Intention of Mobile Learning (M-Learning) in Iraqi Universities Under an Unstable Environment: Integrating the ECM and UTAUT2 Models

Aim/Purpose: This study examines the adoption and continued use of m-learning in Iraqi universities amidst an unstable environment by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Expectation-Confirmation Model (ECM) models. The primary goal is to address the specific challenges and opportunities in Iraq’s higher education institutions (HEIs) due to geopolitical instability and understand their impact on student acceptance, satisfaction, and continued m-learning usage. Background: The research builds on the growing importance of m-learning, especially in HEIs, and recognizes the unique challenges faced by institutions in Iraq, given the region’s instability. It identifies gaps in existing models and proposes extensions, introducing the variable “civil conflicts” to account for the volatile context. The study aims to contribute to a deeper understanding of m-learning acceptance in conflict-affected regions and provide insights for improving m-learning initiatives in Iraqi HEIs. Methodology: To achieve its objectives, this research employed a quantitative survey to collect data from 399 students in five Iraqi universities. PLS-SEM is used for the analysis of quantitative data, testing the extended UTAUT2 and ECM models. Contribution: The study’s findings are expected to contribute to the development of a nuanced understanding of m-learning adoption and continued usage in conflict-affected regions, particularly in the Iraqi HEI context. Findings: The study’s findings may inform strategies to enhance the effectiveness of m-learning initiatives in Iraqi HEIs and offer insights into how education can be supported in regions characterized by instability. Recommendations for Practitioners: Educators and policymakers can benefit from the research by making informed decisions to support education continuity and quality, particularly in conflict-affected areas. Recommendation for Researchers: Researchers can build upon this study by further exploring the adoption and usage of m-learning in unstable environments and evaluating the effectiveness of the proposed model extensions. Impact on Society: The research has the potential to positively impact society by improving access to quality education in regions affected by conflict and instability. Future Research: Future research can expand upon this study by examining the extended model’s applicability in different conflict-affected regions and assessing the long-term impact of m-learning initiatives on students’ educational outcomes.




mobile learning

Performance Expectancy, Effort Expectancy, and Facilitating Conditions as Factors Influencing Smart Phones Use for Mobile Learning by Postgraduate Students of the University of Ibadan, Nigeria

Aim/Purpose: This study examines the influence of Performance Expectancy (PE), Effort Expectancy (EE), and Facilitating Conditions (FC) on the use of smart phones for mobile learning by postgraduate students in University of Ibadan, Nigeria. Background: Due to the low level of mobile learning adoption by students in Nigeria, three base constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model were used as factors to determine smart phone use for mobile learning by the postgraduate students in the University of Ibadan. Methodology: The study adopted a descriptive survey research design of the correlational type, the two-stage random sampling technique was used to select a sample size of 217 respondents, and a questionnaire was used to collect data. Descriptive statistics (frequency counts, percentages, mean, and standard deviation), test of norm, and inferential statistics (correlation and regression analysis) were used to analyze the data collected. Contribution: The study empirically validated the UTAUT model as a model useful in predicting smart phone use for mobile learning by postgraduate students in developing countries. Findings: The study revealed that a significant number of postgraduate students used their smart phones for mobile learning on a weekly basis. Findings also revealed a moderate level of Performance Expectancy (???? =16.97), Effort Expectancy (???? =12.57) and Facilitating Conditions (???? =15.39) towards the use of smart phones for mobile learning. Results showed a significant positive relationship between all the independent variables and use of smart phones for mobile learning (PE, r=.527*; EE, r=.724*; and FCs, r=.514*). Out of the independent variables, PE was the strongest predictor of smart phone use for mobile learning (β =.189). Recommendations for Practitioners: Librarians in the university library should organize periodic workshops for postgraduate students in order to expose them to the various ways of using their smart phones to access electronic databases. Recommendation for Researchers: There is a need for extensive studies on the factors influencing mobile technologies adoption and use in learning in developing countries. Impact on Society: Nowadays, mobile learning is increasingly being adopted over conventional learning systems due to its numerous benefits. Thus, this study provides an insight into the issues influencing the use of smart phones for mobile learning by postgraduate students from developing countries. Future Research: This study utilized the base constructs of the UTAUT model to determine smart phone use for mobile learning by postgraduate students in a Nigerian university. Subsequent research should focus on other theories to ascertain factors influencing Information Technology adoption and usage by students in developing countries.




mobile learning

5 Ways In Which Mobile Learning Helps To Engage During Virtual Training

Virtual learning is effective in disseminating knowledge to learners. But, today, the requirement is not just to disseminate knowledge, but also to engage, retain and […]

The post 5 Ways In Which Mobile Learning Helps To Engage During Virtual Training appeared first on e-Learning Feeds.




mobile learning

The Mobile Learning Voyage - From Small Ripples to Massive Open Waters: 14th World Conference on Mobile and Contextual Learning, mLearn 2015, Venice, Italy, October 17-24, 2015, proceedings / edited by Tom H. Brown, Herman J. van der Merwe

Online Resource