Learners First. An Agile Approach to Learning (eBook)

Vera Baum, Manuel Illi (Autoren)

eBook Download: EPUB

2022 | 1. Auflage
136 Seiten
Books on Demand (Verlag)
978-3-7562-6875-7 (ISBN)

Infos

Digital and agile transformations are learning processes for employees, teams and organisations. Many companies realise that the resulting learning needs cannot be met with standard trainings and other development methods. But how can learning, upskilling and employee development be designed in an agile way? The authors provide science-based answers and practical advice for the implementation of an agile learning approach. They show how learning coaching and agile methods can be used to make learning processes in organisations more efficient, demand-oriented and sustainable, and how a self-directed learning culture can be successfully established.

Dr. Vera Baum studierte Pädagogik mit den Nebenfächern Psychologie und Informatik an der LMU München und promovierte ebenda 2016 zum Thema Lernen mit neuen Medien. Dank ihrer Arbeit in der Forschung und der damit einhergehenden praktischen Umsetzung mehrerer Lernkonzepte und Lernplattformen ist Vera Gehlen-Baum eine gefragte Expertin für computerunterstütztes Lernen. Sie vereint die versierte Perspektive einer forschenden Pädagogin mit der praktischen Erfahrung als Scrum Master, Product Owner und Beraterin. Den Ansatz agilen Lernens stellte Sie im Jahr 2018 vor den United Nations in New York und Genf als Beitrag zu mehr Bildungsgerechtigkeit vor.

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3. Theoretical Foundations

When people hear the term 'agility' they usually think about the practical application of it. Agility was born out of practice, and in most cases it is used by practitioners in companies in the production of tangible products and services. It could hardly be more 'practical'. This is why it is interesting to see that agility ( in general and Scrum in particular ) makes use of diff erent concepts that have been extensively examined by many scholars in various research institutions. In this chapter we take a look at theories and concepts from learning sciences to explain where agility and learning approaches overlap and particularly why agile learning works.

The agile learning approach puts into practice a number of theories and concepts from didactic-psychological research, which have been proven to be eff ective in oft en a large number of studies. To mention a few :

• Motivation theories ( e.g. Brehm & Self, 1989 ; Deci & Ryan, 1985 ; Ryan & Deci, 2000 )
• Reactance ( e.g. Brehm 1966, 1993 )
• Goal-directed learning ( e.g. Heckhausen & Dweck, 1998 ; Lin-Siegler et al., 2016 )
• Roles and scripts ( e.g. Wecker & Fischer, 2007 )
• Self-directed learning ( e.g. Freund et al. 2004 ; Lehmann et al. 2014 )
• Feedback ( e.g. Narciss, 2014 ; Strijbos et al., 2010 ; Veenmann & Spaans, 2005 )
• Metacognition ( e.g. Baker & Lund 1997 ; Verpoorten, et al., 2014 )
• Mindfulness / Awareness ( e.g. Williams, 2013 ; Shapiro & Carlson, 2011 )
• Problem-based learning ( e.g. Hmelo-Silver & Barrows, 2008 ; Barrett & Moore, 2010 )

The following chapters will take a closer look at some of these theories and concepts and their significance for agile learning.

3.1 Learning Goals and Motivation

Agile learning focuses on the individual learner. This is not simply due to an individualistic trend but has sound scientific reason. The basic idea is clear : the closer a learning process is adapted to the individual circumstances of a learner, the better the learning progress and success. A closer look at the considerations behind this basic idea and how to individualise the learning process in the most goal-oriented way will lead the way. The central aspects here are personal dispositions, individual learning goals and the motivation associated with these learning goals.

Every learner is diff erent

Educational psychology has long been concerned with the individual psychological dispositions of learners. Objects of investigation are talent, intelligence, willingness to achieve, persistence or ability to concentrate, to name a few. There is, of course, a reason for this interest : to understand how teaching or learning situations can be designed in such a way that the individual differences of learners can be addressed. The aim of the research is to improve the learning success of each individual and not just the statistical average.

Cronbach ( 1975 ) distinguishes three diff erent ways of adapting teaching situations :

• Adaptation of learning goals to the individual learner ( mastery learning )
• Adaptation through compensatory measures ( e.g. additional learning material for learners with less prior knowledge or inadequate learning strategies )
• Adaptation through adaptive teaching ( the choice of teaching methods depends on the individual prerequisites, skills, prior knowledge etc. of each learner )

The individualisation of teaching / learning arrangements has a proven positive eff ect on the level of motivation ( Schuster, 2017 ) : Learners mainly motivated by avoiding failure can quickly achieve initial success with easier tasks and catch up in the long term ; very talented and high-performing learners motivated by achievements are not underchallenged or bored ( see also the section on 'Achievement and failure motivation' further on in this chapter ).

While it is very diff icult in formal educational contexts to set up a consistent adaptive teaching / learning environment, it is inherent in agile learning with individual learning sprints ( see chapter 5.3 ) and learning support by learning coaches ( see chapter 4.2 ). As research shows, particularly individual learner support has a very positive influence on learning success in the long term. Benjamin Bloom ( 1984 ) for example, was able to show in his study entitled “The 2 Sigma Problem” that students who were supervised one-on-one by a lecturer or tutor scored better in tests by up to two standard deviations than students in reference groups who attended conventional courses ( in one study, students in the experimental group even scored up to 98% better ). Bloom explains this deviation mainly by precisely matched instructions and direct feedback.

Bloom's study pointed out aspects that would be confirmed by later investigations : learning performance is more controllable and less dependent on factors such as intelligence as previously assumed. In other words, every learner is diff erent and has diff erent prerequisites, but this does not say anything about the learning progress.

Contrary to popular opinion, intelligence, talent or innate personality traits play a much smaller role in identifiable learning success than, for example, previous knowledge and the right learning-goal motivation ( Schuster, 2017 ). The crucial factors are the individual design of the learning process ( e.g. individual learning goals, appropriate learning strategy training, suitable exercises ) and the personal support by learning coaches in monitoring, evaluating and reflecting on one's own learning process or by external feedback ( see chapters 3.3 and 3.4 ). This means that the conditions for successful learning can be much more influenced than generally assumed.

This contradicts a widespread, yet incorrect general conviction that still persists : Many people are convinced that intelligence is the decisive factor for learning success and that it is inherent and therefore unchangeable. Carol Dweck ( 1999, 2012 ) has been able to prove that it is this 'intelligence theory' itself that has a negative influence on learning success and motivation. Dweck calls it 'entity theory of intelligence' because intelligence is understood as a fixed entity with extensive influence on learning. Who believes that the essential conditions for learning are hereditary will tend to believe that more eff ort or better methods will hardly lead to a significantly improved learning outcome – neither for themselves as a learner, nor for others as a teacher.

If learners – for example in a planning meeting ( see chapter 5.2 ) – show low motivation or little self-confidence, it may be helpful to point out that intelligence can indeed developed positively and that learning success depends to a large part on the suitable design of the learning process ( Schuster, 2017 ).

The myth of learning types

In educational psychology, there is broad agreement that people bring individual dispositions into the learning processes. Ideally, these dispositions are taken into account when designing learning processes to help improve the individual's personal learning success. This success has a lot to do with motivation when setting learning goals, but also with the motivation in the learning process itself.

It is worth taking a closer look at this aspect because there is also a widespread general belief that cannot be proven empirically : the assumption that there are diff erent 'learning types.' Particularly in guidebooks on learning, such classifications and the corresponding learning strategies and formats are oft en promoted. A typical example is the classification into visual, auditory, haptic and kinaesthetic learning types including a learning type test, as discussed by Geuenich et al ( 2015, p. 102ff . ).

Similar to the 'entity theory of intelligence' mentioned before, 'individual learning types' imply that the way in which people learn particularly well is inherent and cannot generally be changed. Someone with a visual learning type therefore learns better with visual learning strategies and worse with auditory methods – whether they want to or not. But there is no clear empirical evidence for this theory – quite the opposite. The studies of Husmann & O'Loughlin ( 2018 ) show that students who followed learning styles chosen according to their presumed learning type do not at all performed better in tests than students who did not follow any learning style. In one group children who were classified as visual learning types were only provided with visual learning media, while the assumed learning type did not play a role in the other group. The learning outcomes in both groups did not diff er significantly.

So why is the idea of learning types so persistent? Husmann & O'Loughlin ( 2018 ) explain this by an expectation that is shaped early on. Most children and young adults develop the conviction that they are a special learning type. Later, they conclude that their learning performance is better with the appropriate strategies and materials. For example, students who are convinced that they are a visual learning type are also convinced that they can achieve better learning results with visual learning strategies.

A study by Knoll et al. ( 2017 ) explores these preconceptions and shows how strongly the expectation based on this assumption deviates from actual results. In the study, learners who saw themselves as visual learning types did not learn any better with visual strategies than learners who were convinced that...

Produkt-Details

• Erscheint lt. Verlag: 7.6.2022
• Sprache: englisch
• Themenwelt: Sozialwissenschaften ► Pädagogik ► Erwachsenenbildung
• Schlagworte: Agile Learning • Learning and development • learning coach • Lifelong Learning • Self-directed Learning
• ISBN-10: 3-7562-6875-6 / 3756268756
• ISBN-13: 978-3-7562-6875-7 / 9783756268757