Digital onboarding has many advantages over traditional onboarding processes. For example:
It can reduce costs, as it requires less in-person training time and less time spent getting the information new employees need. It can also reduce the costs of paper and ink that would typically be used for materials.
New employees can start working sooner, as they’re not spending their entire first day or their first few days filling out paperwork. Digital onboarding allows this process to be spread out, reducing overwhelm and allowing employees to get on-the-job training and experience quickly.
Digital onboarding can be offered to new hires before they even step foot on-site. That said, bear in mind that many onboarding activities are considered work activities and will still need to be paid; this is something to consider before offering any onboarding that occurs before payroll has been set up for a new hire.
It will ensure that the materials presented to each new employee are consistent because they are kept centrally located.
It will ensure all new hires get the most current version of policies and forms, as the central location can easily be kept up to date rather than having copies in every hiring manager’s hands.
Digital onboarding may make it easier to track which items a new employee has and has not completed, as they’re available online and can be tracked automatically instead of manually.
Employees can complete the entire onboarding process at a pace that is preferable to them, within reason.
It will ensure nothing is missed because checklists and confirmations can be built into the process.
The digital onboarding process can be seamlessly integrated with other learning management software or HRIS the organization uses.
It can provide reference materials for employees to use in the future.
It can help the employer stay in compliance with all legally required forms.
A growing body of evidence has found strong consumer interest in recent months in skills-based, online credentials that are clearly tied to careers, particularly among adult learners from diverse and lower-income backgrounds, whom four-year colleges often have struggled to attract and graduate.
For years the demographics of higher education have been shifting away from traditional-age, full-paying college students while online education has become more sophisticated and accepted.
That has amplified interest in recent months among employers, students, workers and policy makers in online certificates, industry certifications, apprenticeships, microcredentials, boot camps and even lower-cost online master’s degrees.
Moody’s, the credit ratings firm, on Wednesday said online and nondegree programs are growing at a rapid pace.
Google will fund 100,000 need-based scholarships for the certificates, and said it will consider them the “equivalent of a four-year degree” for related roles.
Google isn’t alone in this push. IBM, Facebook, Salesforce and Microsoft are creating their own short-term, skills-based credentials. Several tech companies also are dropping degree requirements for some jobs, as is the federal government, while the White House, employers and some higher education groups have collaborated on an Ad Council campaign to tout alternatives to the college degree.
One of the most consistent findings in a nationally representative poll conducted by the Strada Education Network’s Center for Consumer Insights over the last five months has been a preference for nondegree and skills training options.
Despite growing skepticism about the value of a college degree, it remains the best ticket to a well-paying job and career. And data have shown that college degrees have been a cushion amid the pandemic and recession.
Experts had long speculated that employer interest in alternative credential pathways would wither when low employment rates went away,…. Yet some big employers, including Amazon, are paying to retrain workers for jobs outside the company as it restructures.
For those looking for ways to set podcast assignments : we developed a toolbox to guide students to plan, record and edit a 10-15min episode last year (little did I know I’d be rolling it out in a remote course, but it worked… mostly). Material, prompts and some thoughts on assessment now available on our website :https://blogs.helsinki.fi/podcourse/. Feel free to use it… and let me know how you get on 😉.
Got a new open access article out on the ways AI is embedding in education research. Well-funded precision education experts and learning engineers aim to collect psychodata, brain data and biodata as evidence of the embodied substrates of learning. https://t.co/CbdHReXUiz
This article presents an examination of how education research is being remade as an experimental data-intensive science. AI is combining with learning science in new ‘digital laboratories’ where ownership over data, and power and authority over educational knowledge production, are being redistributed to research assemblages of computational machines and scientific expertise.
Research across the sciences, humanities and social sciences is increasingly conducted through digital knowledge machines that are reconfiguring the ways knowledge is generated, circulated and used (Meyer and Schroeder, 2015).
Knowledge infrastructures, such as those of statistical institutes or research-intensive universities, have undergone significant digital transformation with the arrival of data-intensive technologies, with knowledge production now enacted in myriad settings, from academic laboratories and research institutes to commercial research and development studios, think tanks and consultancies. Datafied knowledge infrastructures have become hubs of command and control over the creation, analysis and exchange of data (Bigo et al., 2019).
The combination of AI and learning science into an AILSci research assemblage consists of particular forms of scientific expertise embodied by knowledge actors – individuals and organizations – identified by categories including science of learning, AIED, precision education and learning engineering.
Precision education overtly uses psychological, neurological and genomic data to tailor or personalize learning around the unique needs of the individual (Williamson, 2019). Precision education approaches include cognitive tracking, behavioural monitoring, brain imaging and DNA analysis.
Expert power is therefore claimed by those who can perform big data analyses, especially those able to translate and narrate the data for various audiences. Likewise, expert power in education is now claimed by those who can enact data-intensive science of learning, precision education and learning engineering research and development, and translate AILSci findings into knowledge for application in policy and practitioner settings.
the thinking of a thinking infrastructure is not merely a conscious human cognitive process, but relationally performed across humans and socio-material strata, wherein interconnected technical devices and other forms ‘organize thinking and thought and direct action’.
As an infrastructure for AILSci analyses, these technologies at least partly structure how experts think: they generate new understandings and knowledge about processes of education and learning that are only thinkable and knowable due to the computational machinery of the research enterprise.
Big data-based molecular genetics studies are part of a bioinformatics-led transformation of biomedical sciences based on analysing exceptional volumes of data (Parry and Greenhough, 2018), which has transformed the biological sciences to focus on structured and computable data rather than embodied evidence itself.
Isin and Ruppert (2019) have recently conceptualized an emergent form of power that they characterize as sensory power. Building on Foucault, they note how sovereign power gradually metamorphosed into disciplinary power and biopolitical forms of statistical regulation over bodies and populations. Sensory power marks a shift to practices of data-intensive sensing, and to the quantified tracking, recording and representing of living pulses, movements and sentiments through devices such as wearable fitness monitors, online natural-language processing and behaviour-tracking apps. Davies (2019: 515–20) designates these as ‘techno-somatic real-time sensing’ technologies that capture the ‘rhythms’ and ‘metronomic vitality’ of human bodies, and bring about ‘new cyborg-type assemblages of bodies, codes, screens and machines’ in a ‘constant cybernetic loop of action, feedback and adaptation’.
Techno-somatic modes of neural sensing, using neurotechnologies for brain imaging and neural analysis, are the next frontier in AILSci. Real-time brainwave sensing is being developed and trialled in multiple expert settings.
the HyFlex model for the fall… reflects a rift between administrators and professors, who are raising alarms over the health risks of teaching in person, and about the logistical, technical, and pedagogical complications of the model itself. Search HyFlex on Facebook and Twitter and you’ll come across comments like this one: “Whoever the hell thought of this is a bean counter, not an educator, and an idiot.”
Teaching experts and others familiar with hybrid teaching say that HyFlex can work, but it requires effective technology, careful planning, instructional support, and creative course design.
“If HyFlex is part of the plan, it has to be done with will faculty participation,” says Brian Beatty, an associate professor of instructional technologies at San Francisco State, who created the model. “Otherwise, if it’s top down and the administration is saying, We’re doing this, then the faculty are saying, But why are we doing this?”
Much of what bothers professors about the push for HyFlex is that so many details about its mechanics remain ill defined. And assumptions about its value seem rooted in a particular idea of teaching, one where the professor stands at the front of a classroom and lectures.
“We are the ones holding the bag if this does not work, or if it’s chaos,” says Michelle Miller, a psychology professor at Northern Arizona University and author of Minds Online: Teaching Effectively With Technology.
Miller is a fan of the original HyFlex model from San Francisco State, but says that colleges need to be mindful that the conditions under which it’s now being adapted — quickly, at scale, and without giving students much choice — will limit its effectiveness.
To work effectively, she says, hybrid teaching requires a lot of support, such as having teaching assistants help manage the complexities of working simultaneously with two different audiences. Otherwise it risks becoming a “lecture-centric, passive consumption view of learning.” That goes against years of hard work faculty members have been doing to make their classrooms more inclusive, active, and engaged.
To help think through pedagogical challenges, faculty groups are testing out teaching strategies, some departments meet weekly to discuss course design, and a student-leadership team is providing feedback and creating online tools to help their peers learn effectively online.Even so, the process has been challenging and frustrating at times for faculty members. Professors are both looking for templates and wanting to maintain control over their courses, which inevitably creates tension with the administration.
In April 2020, the company claimed its G Suite for Education products were used by 120 million students and users across the world. More than 100 million use Classroom, its online collaboration and learning management platform. Over 40 million students and educators across the globe now use Chromebooks.
Ternier, S., Klemke, R., Kalz, M., Van Ulzen, P., & Specht, M. (in press). ARLearn: augmented reality meets augmented virtuality [Special issue]. Journal of Universal Computer Science – Technolgy for learning across physical and virtual spaces.
Augmented reality (AR) and AR games offer a unique opportunity to
implement this core idea in linking real world situations and problems with learning
support. The theory of situated learning [Lave & Wenger, 90] is grounded on the
assumption that learners do not learn via the plain acquisition of knowledge but they
learn via the active participation in frameworks and social contexts with a specific
social engagement structure. Kolb’s learning cycle [Kolb, 84] and the concept of
experiential learning discusses
de Freitas stresses the importance of linking the
experiences made in a game, simulation or micro world with their application in real
world practices [de Freitas, 06]. [Brown & Cairns, 04] describe game immersion as a
continuum from engagement over engrossment to total immersion.
Despite the huge potential of immersive games to overcome the gap between the real
world and the educational context and the rising market for electronic games [PWC,
10], the use of technology-enhanced immersive games in education is still quite low.
The reasons for this are manyfold:
● high game development costs meet limited educational budgets [Westera et
al., 08]
● predefined games are hard to be integrated in the educational process
[Klopfer, Osterweil & Salen, 09]
● learner support in online games does not easily scale [Van Rosmalen et al.,
08]
● furthermore, game platforms up to now could not easily be integrated with
real world environments.
augmented reality browsers like Layar and Wikitude
first mashups for Google StreetView (called StreetLearn) and for mobile
devices which use the Android Google Maps API (called ARLearn). StreetLearn is
intended to provide an augmented virtuality environment on a Desktop, while mobile
devices are provided with an augmented reality experience through ARLearn. By
creating scripts, adding interactive elements and by introducing gamification
elements, we believe that we can increase the learner’s motivation and provide a
richer learning experience linking mobile augmented reality and augmented virtuality.
freely available tools and offers an open REST API. From the enduser
point of view, playing games is easy for users and requires no special knowledge.
Creating scripts requires no programming skills but does impose still technical
background as scripts are to be edited either in JSON or XML.
