Integrating Technology

Student-centered approaches: when effectively designed, digital technologies support the active involvement of students and might improve retention, transfer, and application of knowledge. Online discussions and debates, collaboration, creation, and sharing tools are compatible with problem-solving and inquiry-based strategies.

Personalized learning: technology facilitates the customization of instruction based on individual student’s needs, levels of expertise and/or interests. Certain technology solutions allow instructors and distance learning managers to create “personalized learning paths” letting students make choices and provided them with tailored-made instruction (Schmid & Petko, 2019).

Access: technological devices, tools, and applications enable access for learners with different needs, including students with disabilities, students in rural communities, or from economically disadvantages families, for instance assistive technologies and text-to-speech tools.

Engagement and motivation: digital technology facilitates the use of multimedia (audio and visual), simulations, digital storytelling and game-based learning to promote and sustained motivation, enjoyment and interest.

Communications and interactions: synchronous (videoconferencing) and asynchronous (messaging and forums) facilitate communications and allow meaningful student-instructor, student-student, and student-content interactions.

Learning Management Systems (LMS), or platforms that automate administration, can track learners’ grades, attendance, and progress, and can generate reports. Examples of LMS are BlackBoard®, Canvas®, or Moodle®,

Big Data and Data analytics. The term “Big Data” refers to data “so large or so complex that conventional applications are not adequate to process them” (Sin & Muthu, 2015). Data analytics is focused on collecting, examining, and reporting the big data from students’ online habits and finding context that can be used to evaluate different dimensions of the educational system (e.g, curricula, instruction, services, and technologies). Educational researchers can create predictive models to identify factors that might influence learning behaviors, learning outcomes, motivation, and risk of attrition.

Web/mobile applications. Learning applications that are increasingly being provided as Software as a Service (SaaS). They are adapted to web and mobile environments and some can be integrated into LMS. Popular examples currently include RealizeIt, H5P, Materia, NearPod. They can also function independently and provide mechanisms to collect big data in fine-grained ways, including time spent on activities and resources within pages by user, number of attempts and scores per attempt per learning activity per user, and answers to open-ended or close-ended learning activities.

Artificial intelligence: defined as “computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks.” (Popenici and Kerr, 2017). AI has the potential to support education administration via business intelligent applications and instruction with the use of chatbots, digital agents that provide feedback and guidance, and social robots. Data Analytics models can also be enriched with AI.

Virtual Reality (VR): immersive simulations presented completely with digital graphics and sounds. Typically, VR immersive experiences are provided through VR-adapted headsets/headphones to increase the sense of realism. For example, in the health education field, 3D immersive scenarios could be used to let students practice diagnosis while interacting with patients in a virtual environment.

Augmented reality (AR). Technology that concurrently combines digital information with the physical world (typically through a cellphone camera or tablet). AR allows students to interact with virtual objects/models in the real world, for instance, 3D human bodies and skeletons can be digitally displayed in mobile devices while students physically move through the applications to identify parts of organs. (Pottle, 2019).