The current business climate demands organisational agility and fully leveraging the knowledge capital of its workforce to maintain a business competitive edge. This is likely to continue and become more demanding for the foreseeable future, hence the impetus for big data projects costing millions of pounds.
If organisations are to enhance their competitiveness then better mechanisms are needed to capture employee collaborative learning in situ; to use it to improve productivity, the efficiency of its business processes, and retain and share the generated know-how unique to each organisation (Fischer, 2011)1 amongst its employees to better support their working practices.
While we know that the majority of work-based learning takes place doing the work there are on-going challenges to providing situated training when there is any change to business processes or when new employees join.
A core issue and challenge is how most training is currently delivered in the workplace. For example:
- Face-to-face training sessions are usually delivered to small groups either in-house or off-site.
- This may be costly in terms of resources such as time and money, plus it may not be suitable for all trainees in the group, considering their prior knowledge and specific work needs.
- E-learning provides greater flexibility in delivery because it is usually on a one-to-one basis.
- However, depending upon course content and structure it may prove inadequate to meet individual specific work needs.
- Collaborative learning is the most challenging to capture and deliver. So typically the learner receives in situ training from one of more members in a work team or while watching and listening to what goes on around them.
- Capturing collaborative learning in situ while the work is being undertaken between workers is the most challenging for training. Yet it is essential that the collective pool of workplace knowledge is gathered, shared and grows. True learning support for collaborative worker actions has been the holy grail of e-learning developers, software and usability designers.
We must always be aware that employees rarely work in isolation. The work they receive as inputs are the outputs of someone else’s work; the work they produce as outputs become the inputs for another person’s work. Together they work to fulfil part of a much larger organisational business process that may span one or several departments, both locally and overseas.
In trying to do this there is a lot of ‘active’ learning taking place: on the job, through social exchange and possibly some training. A very large part of this learning is collaborative. The learning and application tend to happen at an intense pace to fulfil the process and improve it where possible. All of this happens iteratively.
Individual members that make up a team will carry out either common or specialised tasks as part of a business process. To complete the task the team works together, pooling their knowledge resources to become collective knowledge across the work environment.
Sharing learning to optimise business processes
A competent team would also be iteratively optimising the process and sharing best practice: learning in the improvement loop. A lot of this ‘know how’ is generated, shared and consumed across a range of media and methods. Conversations, documents, files, links, comments, searches, courses, meetings and so on mean that the collective in situ way of working and learning does not happen in isolation, but is decentralised. It is fragmented and disorganised; and some of it may get lost.
Effectively capturing this ‘learning loop’ and its output is essential to being able to work with a process and make continuous performance improvement. We also need to consider that a team structure is dynamic, with staff joining and leaving the team, and large activities being distributed across multiple teams and outsourcing/ off-shoring (Brown, 2012)2.
The learning interventions we design need to reflect this process orientation. More so, they must meet the competence needs for the processes while also being tailored to the individual proficiency levels of the employee. For example, if an employee is proficient at 50 per cent of a task and they undergo face-to-face training, then 50 per cent of the training is a waste of limited resources: financial and the time lost impacting on work that might otherwise have been done.
There is then the issue of how quickly an employee engages with such interventions, how well-matched their development requirements are with the training offered, how long they take to complete and how quickly they can be applied back into the workplace. These are important issues in realising a more personalised and adaptive approach to achieving corporate learning outcomes (Learning Solutions Magazine, 2014)3.
Introducing a process learning hub
Considering the range of user-generated content and disparate training available, the preferable solution would involve a system that can mediate a ‘process learning hub’. It would allow a centralised body of content, such as a training course or knowledge base, to be customised by employees to deliver a more relevant sequence of learning and performance support to complete a process.
The content in the hub can be further edited by the members of a team to update content and best practice, effectively refining the content as the process is refined. The result is a customised body of knowledge closely aligned to the process or ‘way of working’.
New team members can use it to learn the necessary skills to effectively fulfil processes, existing staff can use it as a reference or support tool. The system would also adapt what is presented to an employee based on familiarity and competence with the process.
Automating data collection to refine collective user learning
The ‘process learning hub’ would store the activity of users across their bespoke content set. This sufficiently large body of data can then be used to understand user behaviour, trends, problem points and possible process issues.
Importantly, such a data set can assist in determining specific performance needs and anticipate how content is interacted with to optimise structure and relevance (Mayer-Schönberger and Cukier, 2014)4. This would be automated through machine learning algorithms.
As more staff engage with and use the hub, the iterative algorithm would improve the selection and delivery of content, providing employees with just what they need to learn to complete processes using collective know-how.
Emerging opportunities for work-based learning
It is long overdue that learning management system vendors incorporate adaptive learning technology into their systems and some now can. A subset of such vendors also provides integration with other platforms such as Microsoft SharePoint and Alfresco, which offer social content management and communication. The kernels of a ‘process learning hub’ could likely already be in your organisation.
Organisations such as yours can realise greater value from the resources you already have and maximise efficiency of your business processes by providing ‘process learning hubs’.
Computer Supported Collaborative Working (CSCW) and Learning (CSCL) need to facilitate collaborative learning that is adaptive so that a team can improve work practices and processes (Goggins et al, 2012)5. This has greater value if the learning can be shared in situ within a group as it works through daily routines of analysis of data-hoc problem-solving scenarios.
- Fischer, G. (2011). Understanding, Fostering, and Supporting Cultures of Participation. Interactions, May-June, ACM. USA.
- Seely Brown, John (2013). Foreword. In: Sean Goggins, Isa Jahnke & Volker Wulf (Eds.): Computer-Supported Collaborative Learning at the Workplace: CSCL@Work. New York: Springer, pp. v - viii.
- Learning Solutions Magazine, (2014). Skillsoft and IBM Research: Harnessing Big Data in Enterprise Learning by News Editor: Learning Solutions Magazine. (online) (Accessed 22 May. 2014).
- Mayer-Schönberger, V. and Cukier, K. (2014). Learning with Big Data: The Future of Education. 1st ed. Eamon Dolan/ Houghton Mifflin Harcourt, p.16.
- Goggins, S., Jahnke, I. and Wulf, V. (2012). CSCL@Work revisited - CSCL and CSCW? Are There Key Design Principles for Computer Supported Collaborative Learning at the Workplace? In GROUP ’12, Sanibel Island, Florida, USA.