In her blog, Ann-Marie Scott speaks on many issues related to learning technologists (or more aptly – the lack thereof), and one revelation that she speaks of is the need to look at how we are using digital technologies currently and investigate how we may be able to take their usage further, moving away from a prescriptive and utilitarian tool, to something that adds value to our ecosystems of learning.
While my context is not higher education, like Ann-Marie’s, I am able to draw parallels to these sentiments, specifically around the use of learning technologies in my corporate or workplace learning settings. Many organizations have been drawn into the web of new learning technologies claiming to offer predictive analytics on learner (employee) performance. The marketing campaigns state that these analytics can help to spot target performance issues by quickly and accurately identifying knowledge deficiencies in individual employees. What I have seen more in practice, is that organizations use the outputs from these technologies as a performance metric, and the resulting use of these platforms is seen by employees as nothing more than a “new KPI on the block”.
These “learning” platforms are often being used as out of the box solutions to increase employee performance, but little thought being given to the other more human factors that impact workplace learning and performance support. Anne-Marie speaks of learning technologies needing to be a part of an eco-system, and not the entire learning universe. If organizations were to look more at learning technologies in this lens, perhaps they would be inclined to look at other aspects of the employee learning ecosystem. Questions like “are the learning materials being uploaded into the learning technology effectively designed?”, or “Are we providing adequate opportunities for employees to develop self-efficacy? (Bandura, 1977)” are important to consider, and could help to transition the reliance on the learning technologies, to the reliance on a learning ecosystem.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
Hi Paula!
Thanks for sharing your corporate perspective on a higher ed topic! I really liked how you highlight how sometimes education takes on a box of solutions to increase employees’ performance. I believe this is also relevant at the K-12 education level. So many times, we are offered solutions that aren’t feasible or require additional training and implementation that are not provided. I could not agree more when you claim “little thought being given to the other more human factors that impact workplace learning and performance support” (Insell, 2022). I also appreciate how you addressed the employees learning through building self-efficacy.
Interestingly, in many professional development sessions my school district provided to us, they drill into us these latest educational strategies and learning theories to improve our teaching and, therefore, our students’ learning. Yet, these workshops are often taught without applying these theories/strategies. I wonder how the outcomes would change if the workshops factored in the approaches they preach to teach us the materials. Would that out of the box solution then be manageable because they invested time into creating competent learners who feel capable because we have the tools and strategies as humans to adapt to the new information and technologies? I wonder if they shifted their perspectives from an academic to a learner how that would change the outcome of motivating their learners.
Thanks for sharing!
-London
While I touched on the practice of data collection in learning technology for workplace learning in my previous post, unit 3 readings really brought home how ethical and moral data collection is a growing area of concern (and opportunity) for corporate educators. Like open distance and eLearning (ODeL) institutions, workplaces are in a heavy change state at present, with decisions currently underway in various industries about the nature of work and learning. Specifically, many employers are choosing to stay in a hybrid work/learn from home model, and as such many of the learning decisions made will depend on efficiency, learner data collection, and employee performance metrics.
Prinsloo and Slade (2014) speak of the need to use these learner analytics to increase student success and retention, and this is a shift that is also required to take place in corporate learning. At present, I have seen data analytics be used to measure adherence to schedule (how long is a learner spent reviewing learning material), likelihood of role performance (how many attempts does it take for a learner to succeed in an assessment activity), and motivation (how often is the learner engaging with material). This is a mindset that must shift.
I believe that through the development of analytic policy at an organizational level, learning leaders could leverage this data to proactively reach out to learners whose data suggests they may be struggling. Instead of inquiring why a learner spends so little time reviewing material, these metrics could be spun to ask, “is the workload accommodating enough to encourage learning?”. Instead of looking at pass/fail metrics as a translation of role success, policy changes could encourage leaders are providing the additional support where needed. When looking at student engagement, altering our data mindset could encourage us to look at the learning resources available opposed to instinctively jumping to the issue being on the shoulder of employees. I believe that data should be used to encourage deeper investigation into organization learning challenges, but should not be exclusively used to institute surface level change.
Data collection in my context has value; but interpreting data in a restorative manner rather than a punitive manner will require a leader who is adaptable and is able to look at the data as a small part of a learning organization whole.
Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. The International Review of Research In Open And Distributed Learning, 15(4), 306-331.
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