Best and Next Practices in Online Education: Opportunities and Challenges

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https://hdl.handle.net/10125/52007

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    Enabling Workers to Enter Industry 4.0: A Layered Mobile Learning Architecture
    (2018-01-03) Engelmann, Andreas; Schwabe, Gerhard
    Manufacturing companies have to meet a lot of challenges in continuing training for employees. Especially on the way towards industry 4.0 the workforce needs to be able to handle fast changing environments and ever-changing working contexts. Furthermore, they have to be familiar with constantly new technologies (e.g. complex user interfaces, mobile devices) that are introduced during the process of company development. Due to this, working people are facing a lifelong learning process and need to evolve to knowledge workers. To fulfill these requirements new concepts are necessary for human resources development directly at the workplace and therefore adequate artifact designs. In this paper we design a layered architecture for mobile learning at the workplace. This layered approach offers the possibility to educate employees with different qualifications and skills using an integrated solution. Further, we propose to implement appropriate components at each layer to support different kinds of learning.
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    Flow in Business Simulation Games: Comparison between Online and Face-to-Face MBA
    (2018-01-03) Kim, J.B. (Joo Baek); Watson, Edward
    This study explores the role of flow and its relationship with other elements of Business Simulation Games (BSGs) used in different MBA course delivery methods, namely online vs. face-to-face (F2F). We collect level of flow and other game behavioral variables from young professionals enrolled in an MBA Technology and Operations Management course. We analyze the data with one-way ANOVA to explore flow measures across different course delivery methods. The findings show there exist differences in flow level and performance measures between online and F2F formats.
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    AI Thinking for Cloud Education Platform with Personalized Learning
    (2018-01-03) Rad, Paul; Roopaei, Mehdi; Beebe, Nicole; Shadaram, Mehdi; Au, Yoris
    Artificial Intelligence (AI) thinking is a framework beyond procedural thinking and based on cognitive and adaptation to automatically learn deep and wide rules and semantics from experiments. This paper presents Cloud-eLab, an open and interactive cloud-based learning platform for AI Thinking, aiming to inspire i) Deep and Wide learning, ii) Cognitive and Adaptation learning concepts for education. It has been successfully used in various machine learning courses in practice, and has the expandability to support more AI modules. In this paper, we describe the block diagram of the proposed AI Thinking education platform, and provide two education application scenarios for unfolding Deep and Wide learning as well as Cognitive and Adaptation learning concepts. Cloud-eLab education platform will deliver personalized content for each student with flexibility to repeat the experiments at their own pace which allow the learner to be in control of the whole learning process.
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