Employees are increasingly expected to organize their own human resource development activities. To what extent and how exactly employees in various organizational contexts manage to shape their individual learning paths however remains largely unclear. The purpose of this present study is to explore, leaning on the empirical Learning-Network Theory (LNT) research and its findings, how employees in different occupations create learning paths that are attuned to their specific work context.
The paper reviews 23 MSc theses based on 14 distinct data sets collected between 2005 and 2015, containing approximately 1,484 employees from some 45 organizations and across various professions. The teachers, nurses, postal, software, telecom, railway and logistics company employees were mostly based in the Netherlands. The analysis focuses on learning-path types and learning-path strategies found in the 23 studies.
Motives, themes, activities, social contexts and facilities were found to be instrumental in explaining differences among individual learning paths. A total of 34 original learning-path types and strategies were found to cluster under 12 higher-order labels. Some of these were based on learning motive, some on learning theme, some on core learning activities, some on social learning context and a few on a combination of these elements. Overall, the socially oriented learning-path strategy was the most prevalent, as it was found among nurses, employees of software/postal/telecom, railway and logistics company employees, as well as teachers in two schools.
The paper presents the first overview of empirical studies on employee learning path(s) (strategies). In addition, it strengthens the empirical basis of the LNT.
Poell, R., Lundgren, H., Bang, A., Justice, S., Marsick, V., Sung, S. and Yorks, L. (2018), "How do employees’ individual learning paths differ across occupations?", Journal of Workplace Learning, Vol. 30 No. 5, pp. 315-334. https://doi.org/10.1108/JWL-01-2018-0019Download as .RIS
Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited
In a knowledge society that requires individuals to update their competences and skills, employees are increasingly expected to organize their own human resource development (HRD) activities to sustain and enhance their career prospects. These include work-related learning activities that are both on-the-job and outside of the workplace. To what extent and how employees in various organizational contexts do this remains largely unclear, although research efforts around this topic have increased over the past 20 years (Choi and Jacobs, 2011; Doornbos et al., 2008). Poell et al. (2000) provide a situated approach to work-related learning and present a theory of learning networks that are formed by interactions among various learning actors and shaped by the dynamics among these actors and work characteristics. The Learning-Network Theory (LNT) acknowledges various strategies for organizing learning and views employees as central actors in the co-organization of their learning related to work rather than passive recipients of a predetermined training activity (Poell et al., 2000, p. 32). Employees are therefore seen as having more ownership and responsibility over their own professional development.
From the perspective of the LNT (Poell and van der Krogt, 2015, 2017a, 2017b), individual employees are seen to create and drive various types of learning paths. van der Krogt (2007a, 2007b) introduced the concept of a learning path (“leerweg” in his original Dutch book) to refer to “a set of learning activities that are both coherent as a whole and meaningful to the employee” (Poell and van der Krogt, 2010, p. 217). This learning-path concept can be used to describe and understand how each individual employee makes sense of the multitude of work-based and intentional learning experiences and their choices as they move from one such experience to the next in their organizational context. Nevertheless, to date, little empirical research has been published to support this assumption. Providing more clarity about employee learning paths can help organizations, HRD practitioners and individuals create learning activities that are better attuned to their specific work context and the choices of employees to enhance their development.
Over the past decade, a large number of unpublished Master’s theses have been conducted to gather empirical evidence of the existence, prevalence, elements, interaction and types of learning paths created by employees in various occupations. The current paper presents a review of these studies and will provide an answer to the following research question: How do employees’ individual learning paths differ across occupations?
Theoretical background: learning-path types and strategies
Although individual learning paths of employees have been conceptualized for over a decade now (Poell and van der Krogt, 2010), little is known about their empirical basis. Poell and van der Krogt (2014, p. 2) use the LNT as a lens to examine the functions of HRD and acknowledge how employees can engage in the deployment of personal development plans and learning-path creation. Although the LNT assumes that individual employees create various types of learning paths in different contexts (Poell and van der Krogt, 2010), no a priori theoretical categorization of learning-path types has been put forth to date. These authors did initially distinguish four elements that characterize every individual learning path:
learning theme, or subject that the employee learns about;
learning activities, or experiences from which employees learn;
social context, or the people with whom employees interact while learning; and
learning facilities, or means of organizational support that employees receive.
The authors also assumed that these four elements of individual learning paths are mutually related. By combining elements in different ways, employees can create their individual learning paths in various ways (Khaled, 2008; Poell, 2005; Sloots, 2008). In a study with nurses in The Netherlands, Poell and van der Krogt (2014) established four distinct types of learning paths, which were based on the similarities among the nurses’ learning-path elements:
formal-external learning path; learning in formal settings and from people in educational settings;
self-directed learning path; emphasizing the individual employee as central to the social learning context in addition to their managers;
social-emotional learning path; learning about providing counseling and coaching to patients and their family members; and
information-oriented learning paths; learning from theory, reflection, or experts within their social learning context.
Nurses were found to create their own individual learning paths by selecting a theme relevant primarily to them, conducting a variety of learning activities around this theme, participating in social contexts that might help them, and mobilizing learning facilities provided by their organization (Poell and van der Krogt, 2014).
In more recent years, learning motives were included as a fifth element of an individual employee’s learning path. Markus (2006), who studied nurses and their learning in different hospital wards, states that learning motives explain what drives employees’ learning efforts. Although mutually related, three learning motives can be differentiated: learning for career development, e.g. for getting a promotion or a salary increase; learning for professional development, e.g. for developing the profession and gaining more expertise; and learning for social functioning, e.g. for keeping up with colleagues and receiving line management appreciation. Various studies show that the learning motive “professional development” has a positive relation on the learning activity “learning through theory” and that career development correlates with the learning activity “learning by adding something new in the job” (Geerts, 2006; van der Pol, 2011; Vercoulen, 2006).
These different learning motives are relevant to the study of learning paths because they “lift” them to a more strategic level within the individual. If and when employees create a learning path out of a specific motive (e.g. to perform a task better, to find a more rewarding job or to make their current projects more satisfying) and attune their learning theme, activities, social contexts and/or facilities to this motive, they can be said to act strategically in creating their learning path (Poell and van der Krogt, 2014). Employees can strategically direct and determine the contents of their learning paths in various degrees (van der Krogt, 2007a, 2007b).
The discussion of learning paths has thus evolved in recent years to include employees’ learning motives beyond “just” their learning theme, activities, social contexts and facilities, emphasizing more the strategic nature of employees’ learning-path creation. Before employee learning paths were conceptualized by van der Krogt (2007a, 2007b), LNT was used primarily as a basis for examining strategies of HRD professionals in organizing learning programs (Poell et al., 2003). As employees have come to take more ownership in their professional development in recent years, it is becoming increasingly important to examine employees’ professional development strategies from the perspective of individual learning paths.
A learning-path strategy can be understood as the direction and ways in which employees choose to guide their own professional development (van der Krogt, 2007a, 2007b). HRD professionals responsible for staff development and training could more effectively reach goals by considering that employees act strategically with respect to their professional development and that these strategies may vary from the professional development strategies that managers and educators intend for them (Nolan et al., 2000; Poell, 2017; Munro, 2008).
Aim and problem statement
The current paper will examine and discuss patterns (or lack thereof) across learning-path types and learning-path strategies emerging among samples of employees in various fields of work. It will present learning-path types and strategies that appear to be common across various occupations regardless of differences in the nature of work, or unique for certain types of occupations. As the LNT expects the organization of learning to be related to the organization of work (Poell et al., 2000), it would make sense if learning paths were found to differ across occupations. However, as learning paths are also tied to the individual employee (perhaps even more so than to the work organization), there may be similarities found across occupations as well. Through the review of recent empirical studies on learning-path types and learning-path strategies, this paper aims to discuss the overall concept of an individual learning path in relation to various contexts within which employees work.
This study examines and compares empirical LNT studies published as Master’s theses over the past 10 years. To provide some consistency in the data sources, all theses were supervised by the first author of the current paper. Second, to be selected as a part of this review, the work had to be empirical and have a specific focus on individual learning paths. Purely conceptual/theoretical studies were not included. The research team consisted of professors and students linked to two universities (one of which was the institution where the original theses had been written), who have shared an interest in studying work-related learning for a considerable number of years (Poell et al., 2009, 2010; Lundgren and Poell, 2016; Lundgren et al., 2017).
Based on the aforementioned criteria, 23 studies were found in the archives of one Dutch university (Table I).
All studies were completed between 2005 and 2015, nine of which were written in Dutch and 14 in English. The length of the theses varied, ranging from 25 to 45 pages. Out of the 23 studies, one thesis was a co-authored primary analysis (Natte and Lisman, 2006), eight were single-authored primary analyses (Laming, 2007; Schietecat, 2007; Gajadhar, 2007; Habets, 2007; Schriever, 2009; de Rooij, 2008; Levels, 2011; van Rijswijk, 2012), ten were single-authored primary analyses in which the data had been collected in four student teams (with Boomaars, 2008; de Hoon, 2008, and Hendrix, 2008, forming a first team; Jacobs, 2010, and Lambert, 2011 a second; de Koning, 2012, and van Bussel, 2012, a third; and Figge, 2012, van Roekel, 2012, and Hendriks, 2013, forming a fourth team), and four theses were single-authored secondary analyses of data that had been collected in other MSc studies (van Kranenburg, 2013; Witteveen, 2013; Kooistra, 2015) or elsewhere (Franken, 2015). Table I summarizes the selected learning path studies.
More than half of the studies were conducted in education contexts, focusing on learning paths of teachers. Therefore, the findings of this study may have largely been shaped based on the experiences of teachers. It could be argued that teachers, as education professionals in the business of making young people learn, bring a certain “sensitivity” with them vis-a-vis the topic of learning paths. This potential shortcoming – or possible opportunity – will be discussed further in the limitations section of the paper.
Data were analyzed by the seven members of the research team in three phases:
Phase 1 – Exploration of Data: During this phase, all 23 theses were read and researchers familiarized themselves with the data and the contexts in which the theses were written. Each thesis was reviewed using a standardized data collection form (Appendix 1). A table of comparison was then established, based on the categories in this form (Appendix 2). This table was used to make decisions about clustering in the next phase.
Phase 2 – Comparison and Verification of Clusters: Next, 13 studies were selected that focused specifically on learning-path types and learning-path strategies (see right-hand column in Table I). Generally speaking, in this phase the team looked for consistencies and inconsistencies in the ways learning-path types and strategies had been labeled across the theses; once that was done, all data could be classified and used for further analysis. More concretely, the team first counted the unique labels for types and strategies combined. The team then analyzed similarly named labels to find types and strategies that could be clustered together. By reviewing the learning-path elements underlying the types or strategies, the team checked various identifiable features to confirm whether some learning paths with similar terms could be clustered together. The team also analyzed whether different labels with similar terms could be combined into new clusters. The terms used for the learning path expressed in the theses alone were not sufficient to complete the analysis. The team, therefore, reviewed the studies again to analyze all underlying learning-path elements that include motives, themes, activities, context and facilities, using a standardized learning-path coding scheme (Appendix 3). For example, the team found that different labels were used for a learning-path type that used theory or formal learning (coded as “A3 theory (formal setting)” as its main theme, and hence clustered them together as “formal course-based”.
Phase 3 – Analysis across occupations: The team finally checked each new learning-path type and strategy cluster and counted the number of occupations or organizations in which these learning-path types and strategies appear. These types and strategies were then analyzed based on the levels of prevalence among the represented occupation types, which are discussed in the findings section.
Findings: learning-path types and strategies across occupations
As an outcome of the analysis (Phases 1 and 2), seven new learning-path types and five new learning-path strategies were found across all 13 theses. Table II highlights the new clusters that were found across all theses investigated in the present study, including the initial labels that the thesis authors had originally put forward. A total of 34 original labels (right-hand column) were clustered into 12 higher-order clusters (left-hand columns), some of which were based on learning motive, some on learning theme, some on core learning activities, some on social learning context and a few on a combination of these elements (see “Description” column).
The learning-path elements help us understand the differences among the 12 newly established clusters for learning-path types (described in terms of activities, themes, social context and facilities) and learning-path strategies (described using the same elements but with motives added). For example, four learning-path strategies are informed by the learners’ motives: EM, EX, PR and SC. In the employability learning-path strategy (EM), the career development motive is strong in comparison to the socially oriented learning-path strategy (SC) where employees are motivated to learn for social functioning and through social interaction. When an employee learns for organizational functioning, the learning-path strategy is more externally driven (EX) in comparison to the profession-driven learning-path strategy (PR), which is more motivated by developing the profession and expertise. The formal course-based learning-path type (FC) and the individual-driven learning-path strategy (ID) are both informed by the activity of learning through theory in a formal setting. ID, however, also draws from trying new and extra activities which makes this learning-path strategy different for each individual. Both private-oriented (PV) and self-directed (SD) learning-path types are informed by their social context: learning from one’s private life with people from one’s private life and outside of work inform PV, whereas SD is learning by oneself within the work context using reflection as an important learning activity. Finally, four learning-path types are informed by their specific theme: COE is informed by learning from experience in the customer domain, IN is informed by learning in the innovation domain, SF in the safety domain and SK in the job specific skills domain.
Prevalence of learning-path clusters by occupation
Figure 1 indicates the extent to which the various learning-path types and strategies were prevalent across occupations represented in the studies reviewed as an outcome of Phase 3 of the analysis. Nurses as well as teachers were investigated in several organizations (hospitals, respectively schools). It is important to note again that the majority of the sample referred to teachers. Overall, the socially oriented learning-path strategy was the most prevalent, as it was found among nurses, employees of software/postal/telecom, railway and logistics companies as well as teachers in two schools. The private-oriented and customer-oriented experiential learning paths were the least prevalent overall, the first one being found only among teachers in one school and the latter only among railway company employees in one study. The others were in between these two extremes, as Figure 1 shows.
Conclusions and discussion
The aim of this paper was to answer the following research question: How do employees’ individual learning paths differ across occupations? A review and analysis of 23 studies (previously unpublished MSc theses) was conducted to this end. A total of 12 new learning-path clusters emerged from this analysis. This indicates that employees create their own individual learning paths and that they choose various ways or strategies of doing so. In answer to the research question, employees’ individual learning paths were found to differ across occupations to a considerable extent. Three learning paths were found in only one profession, six occurred in only two professions, two in three professions and only one was found across all five professions. This finding shows that despite learning paths being created by an individual employee, their organizational context matters in determining what shape the learning paths take.
Theme-based clusters (learning in specific substantive domains) were generally less prevalent than the clusters that were based on other elements, particularly compared to motive-based clusters (learning out of a specific motive). On the whole, the different learning-path types (based on learning themes, activities, social contexts and facilities) were less prevalent than the different learning-path strategies (based mostly on learning motives and activities). Overall, a substantial number of (especially motive-based) clusters were most prevalent across organizations and occupations, while some other clusters (especially theme-based) were found to be more specific to only one or two organizations and occupations. A possible explanation for this might be that relevant themes are on the whole more specific to certain (organizational) contexts, whereas motives are very closely linked to the employees themselves regardless of their specific (organizational) contexts. In other words, taking into account employees’ learning-path motives brings the individual perspective to the fore, whereas for learning-path types (without knowing employees’ motives), the organizational context may be a much stronger explanatory variable. More research is needed to investigate this issue further.
However, this was a first attempt to look at similarities in learning paths across occupations and categorize them using new labels. Perhaps we should really be looking, at this stage, for some underlying principle or difference that needs to be investigated in future research. One difficulty is that we do not know how much the context (or the MSc students’ own meaning making) influenced the naming of the learning-path types and strategies in their theses. Of course, in writing the current paper, we found ourselves in the same situation as did the authors of these research studies, that is, in pursuit of making meaning we might have been pushing too soon for categorization that may or may not stand up over time. More research is needed here as well.
On a related note, the effort to name the specific phenomena that gave rise to the learning-path types and strategies appears to us to be a key in itself. The struggle to identify and cluster these ideas and results from empirical studies points to the formation of new structural orientations in the field. This recalls Fenwick’s (2000) problematization of the urge to typologize observations. Nevertheless, as a first attempt using a novel methodology (pulling from the twenty-first century ethnography, aka “crowd sourcing”), the present review does begin to identify ways to talk about emergent trends in workplace learning, namely, the DIY ethos of individual employees strategically creating their own learning paths.
One key contribution that the current paper makes is bringing a good deal of nuance to the earlier conclusions by Poell and van der Krogt (2014), who found no statistically significant differences in nurses’ learning-path types neither between two hospital types nor among three different departmental types. The present study shows that some learning paths are found in almost all organizations and occupations, whereas others are restricted to only one organization or occupation among the many that were investigated. This implies that learning paths are not entirely dependent on individual employees engaging in their own idiosyncratic ways of learning, as Poell and van der Krogt (2014) had suggested; rather, learning paths are impacted by the organizational and occupational context as well. Further research could investigate to what extent and under what conditions the individual or the contextual influence on discretionary learning activities is more prominent (Lam, 2000).
A first limitation to our study is the large number of teachers in our overall sample. It is possible that teachers exhibit a certain sensitivity towards learning or at least a degree of specificity in regard to the motives. Further investigation of the clusters that emerged (or did not emerge) among teachers versus the other occupations could shed some more light on this issue. The large number of teachers in our sample places limitations on the comparability of learning paths across sectors; it also raises the question of whether the differences within this specific occupation could be explained from a different point of view. Once more, further research will be needed to address this issue.
Other limitations of the present study that should be acknowledged include the overall cultural context in which the thesis investigations were conducted (restricted to The Netherlands). It is also possible that there was some selection effect in the occupations that were included, due to the limited access that the primary researchers had to different sorts of organizations.
A few more technical limitations and clarifications need to be mentioned as well. Clusters based on theses 20, 21 and 23 were counted only once (although for three schools) even if they occurred two or three times, since 20 and 21 were secondary analyses of 18. As Appendix 2 shows, in three cases learning-path types/strategies considered different in the original theses ended up being deemed similar in the present analysis. In four cases, a learning-path type from the original theses ended up being part of a learning-path strategy cluster; vice versa occurred in one case (also indicated in Appendix 2). The more general issue here is to what extent learning-path types and strategies can be merged at all like we have done.
We do think, however, that the 12 clusters found in this paper can help make sense of the enormous variety in learning paths that employees can create across occupations. Of course there may be other potentially relevant learning paths that were not included in the present study because of its focus on these 23 theses.
Implications for further research
An important question for further research is what other factors play a role in explaining differences in learning-path types/strategies. Such other factors could include gender, age, education level, personality, position in the organization, industry sector, country and national culture, just to name a few. We did not take any of these demographic factors into account for the present paper, but numerous studies among the 23 theses under investigation here have discovered connections between these background variables and individual learning paths. For example, the variable “age” has been well researched, where studies focus either on chronological age of the learner or on “future time perspective”. According to Cate and John (2007), future time perspective measures the way that employees experience the future in terms of time of opportunities and/or limitations. For example, a 59-year-old knowledge worker might see the next five working years with plenty of development opportunities, whereas a 35-year-old industry worker might perceive career options as limited. As research suggests that future time perspective influences an employee’s motivation to engage in learning activities and hence form different learning paths, this might be an interesting area for further research. For example, a study conducted among 215 knowledge workers in Austria found that employees with an opportunity focus – as opposed to a limitations focus – were more eager to collaborate and engage in the activity of learning from others (Froehlich et al., 2015). That study also found that opportunity focus had an indirect positive effect on employability. The correlation between chronological age and future time perspective was assumed to be strong and hence looking at chronological age across learning-path studies to explain possible difference in types and strategies would be a logical next step.
Another suggestion for future research would be to use “big data” analytics to seek out insights into the multiple relationships and contextual variables in relationship to performance outcomes. This would allow us to identify which of the variables seem to matter more to performance and whether accommodating learning-path preferences is central to learning gains needed for performance.
Other relevant questions to be answered in further research include the following: Can we compare learning-path types found with learning-path strategies found; how are they related or even similar? Are motives (of learning-path strategies) the “new” way of indicating themes (of learning-path types)? Are there any higher-order clusters, that is, can the 12 clusters meaningfully be grouped into fewer subsets? Can a standardized instrument be designed to measure the different learning-path types/strategies across a large sample of different occupations? Do these 12 clusters (or fewer subsets) hold up when additional data are collected in other types of occupations? Finally, how are the learning-path types and strategies related to the other elements in the learning-path cycle that van der Krogt (2007a) had described: the existing HRD structure and network of HRD actors giving rise to employees’ learning-path creation? Addressing this question might enable us to shed light on the extent to which, and the conditions under which, individual and/or contextual influences affect employee learning.
Selected learning path studies included in data collection
|Author (Year)||Research question (shortened)||Occupations||Types||Strategies|
|1||Natte and Lisman (2006)*||Which learning-path types among nurses can be distinguished based on the relationships among their learning-path aspects?||Nurses||4|
|2||Gajadhar (2007)*||Which learning-path types among experienced teachers in secondary education can be distinguished?||Teachers||4|
|3||Habets (2007)*||In which respects do learning paths differ across occupational groups?||Customer service staff and operators of logistics company||4|
|4||Laming (2007)*||Which learning-path types can be distinguished in various departments?||Train operators, conductors and their educators||6|
|5||Schietecat (2007)*||Which learning-path types among teachers can be distinguished based on the relationships among their learning-path aspects?||Teachers||Nil|
|6||Boomaars (2008)||To what extent are learning motives of employees related to employability activities?||Managers and staff of software, postal and telecom companies|
|7||de Hoon (2008)*||What relational patterns exist between learning motives and actual learning activities of employees?|
|8||Hendrix (2008)*||What are the relationships between learning path motives and learning-path strategies?||5|
|9||de Rooij (2008)*||To what extent are learning structures related to individual learning paths of teachers?||Teachers||4|
|10||Schriever (2009)*||To what extent do perceived learning structures and psychological climate relate to learning-path strategies?||Teachers|
|11||Jacobs (2010)||To what extent does the management and HR-department support the way teachers shape their learning activities?||Teachers and their managers|
|12||Lambert (2011)||Which learning-path strategies do teachers use to achieve professional development goals?||4|
|13||Levels (2011)||Do conflicting interests between school management and teachers have an influence on the learning paths of teachers?||Teachers|
|14||van Rijswijk (2012)||To what extent is learning culture related to an employee’s learning-path strategy and job satisfaction?||Employees in various industries, incl. retail, chemical and high tech|
|15||de Koning (2012)||What is the relationship between the degree of fit, between learning action theories of employees and their actual learning paths?||Teachers and their team leaders|
|16||van Bussel (2012)||To what extent are teachers’ learning-path strategies in line with how teachers should learn according to their managers?||4|
|17||Figge (2012)||Which types of learning paths can be defined based on learning theme, learning activities, learning context and learning facilities?||Teachers||Nil|
|18||van Roekel (2012)||Which type of learning-path strategies can be distinguished based on the relationship between learning motives and learning activities?||4|
|19||Hendriks (2013)||How are teachers’ perceptions of their supervisors’ leadership styles related to their motives to create a learning path?|
|20||van Kranenburg (2013)||To what extent are learning-path strategies of teachers in line with managers’ intended learning-path strategies for teachers?||2|
|21||Witteveen (2013)||What are the motives of teachers to invest in their professional development?|
|22||Franken (2015)||What is the relationship between the dominant actor and the level of reflection within learning paths?||Radiologists;
Managers of medical device company
|23||Kooistra (2015)||How do teachers operate strategically in linking their learning motives to the way they create learning-paths?||Teachers||1|
Indicates that a thesis was written in Dutch
Clustering 34 initial learning-path types and strategies under 12 higher-order labels
|Clusters (n = 12)||Description||Labels (n = 34)|
|COE||Customer-oriented experiential||Learning from experience in the customer domain||Customer-oriented; Experience-based|
|EM||Employ-ability*||Learning for career development and employability||Employability* (2x); Career-driven*; Career-oriented*; Security-driven*; Challenge seeker|
|EX||Extrinsically driven*||Learning for organizational functioning||Extrinsically driven*; Pressure-driven*; Organizational-driven*|
|FC||Formal course-based||Learning through theory in a formal setting||Formal-didactic (2x);
Course-based; Formal-external; Instruction-based; Information-oriented
|ID||Individual-driven*||Individual learning through theory and by trying new/extra activities||Individual-driven*; Personal driven*; Person-centered; Personal-entrepreneurship*|
|IN||Innovation-oriented||Learning in the innovation domain||Innovation-oriented (2x)|
|PR||Profession-driven*||Learning for professional development||Professional-driven*
(3x); Profession-driven*; Professionally driven*
|PV||Private-oriented||Learning from one’s private life with people from one’s private life||Private-oriented|
|SC||Socially oriented*||Learning for social functioning and through social interaction||Socially oriented* (2x); Socially driven*; Social-didactic*; Social-emotional; Social/team-based; Organization-focused|
|SD||Self-directed||Learning by oneself||Self-reflective (2x); Self-directed|
|SK||Skill-oriented||Learning in the skills domain||Skill-oriented (2x)|
|SF||Safety-based||Learning in the safety domain||Safety-focused|
Indicates learning-path strategies (i.e. containing learning motives as a fifth element; all others refer to learning-path types)
Form used to compare selected studies
|Part of data set/cluster of theses|
|Main research question|
|Relevance to learning path analysis||Learning-path types?
|Sample size||Number of organizations:
Number of respondents:
|Research design, incl. data collection method||Design (quantitative, qualitative, mixed):
Data collection instruments (interviews, surveys, observations, other):
Primary or secondary analysis:
|Outcomes||Types of learning paths found? (Which ones?)
Elements of learning paths found?
(Which ones, describe sub-elements)
Types of learning-path strategies found?
(Which ones, include labels)
|How were learning-path strategies determined?|
|Evidence of employees operating strategically in learning and development? (Describe)|
|Strategies of other actors in learning and development, e.g. managers, HRD practitioners other (Describe)|
|Limitations of the study|
|Any other comments|
Comparison of selected learning path studies, in chronological order
|Author (Year)||Research question (shortened)||Occupations||Sample size (N)||Qualit||Quantit||Approach||Types||Strategies||Outcomes:
|1||Natte and Lisman (2006)*||Which learning-path types among nurses can be distinguished based on the relationships among their learning-path aspects?||Nurses||89||x||x||Interviews||4||Formal-external Information-oriented Self-directed Social-emotional|
|2||Gajadhar (2007)*||Which learning-path types among experienced teachers in secondary education can be distinguished?||Teachers||32||x||x||Interviews||4||Formal-didactic Innovation-oriented Private-oriented Self-reflective|
|3||Habets (2007)*||In which respects do learning paths differ across occupational groups?||Customer service staff and operators of logistics company||24||x||Interviews||4||Organization-focused Person-centered Safety-focused Skill-oriented|
|4||Laming (2007)*||Which learning-path types can be distinguished in various departments?||Train operators, conductors and their educators||40||x||x||Interviews||6||Course-based Customer-oriented Experience-based Instruction-based Skill-oriented Social/team-based|
|5||Schietecat (2007)*||Which learning-path types among teachers can be distinguished based on the relationships among their learning-path aspects?||Teachers||24||x||x||Interviews||Nil||No types found|
|6||Boomaars (2008)||To what extent are learning motives of employees related to employability activities?||Managers and staff of software, postal and telecom companies||405||x||Survey|
|7||de Hoon (2008)*||What relational patterns exist between learning motives and actual learning activities of employees?||x||Survey|
|8||Hendrix (2008)*||What are the relationships between learning path motives and learning-path strategies?||x||Survey||5||Extrinsically driven Individual-driven Profession-driven Security-driven Socially oriented|
|9||de Rooij (2008)*||To what extent are learning structures related to individual learning paths of teachers?||Teachers||32||x||x||Interviews and survey||4||Formal-didactic Innovation-oriented Self-reflective Socially oriented|
|10||Schriever (2009)*||To what extent do perceived learning structures and psychological climate relate to learning-path strategies?||Teachers||98||x||Survey|
|11||Jacobs (2010)||To what extent does the management and HR-department support the way teachers shape their learning activities?||Teachers and their managers||35||x||Interviews|
|12||Lambert (2011)||Which learning-path strategies do teachers use to achieve professional development goals?||x||Interviews||4||Employability Organizational-driven Personal entrepreneurship Professional-driven|
|13||Levels (2011)||Do conflicting interests between school management and teachers have an influence on the learning paths of teachers?||Teachers||22||x||Interviews|
|14||van Rijswijk (2012)||To what extent is learning culture related to an employee’s learning-path strategy and job satisfaction?||Employees in various industries, incl. retail, chemical and high tech||489||x||Survey|
|15||de Koning (2012)||What is the relationship between the degree of fit, between learning action theories of employees and their actual learning paths?||Teachers and their team leaders||29||x||Interviews|
|16||van Bussel (2012)||To what extent are teachers’ learning-path strategies in line with how teachers should learn according to their managers?||x||Interviews||4||Career-oriented Professionally driven Social-didactic Socially driven|
|17||Figge (2012)||Which types of learning paths can be defined based on learning theme, learning activities, learning context, and learning facilities?||Teachers||56||x||Interviews||Nil||No types found|
|18||van Roekel (2012)||Which type of learning-path strategies can be distinguished based on the relationship between learning motives and learning activities?||x||x||Interviews and survey||4||Career-driven Personal-driven Pressure-driven Professional-driven|
|19||Hendriks (2013)||How are teachers’ perceptions of their supervisors’ leadership styles related to their motives to create a learning path?||x||Interviews|
|20||van Kranenburg (2013)||To what extent are learning-path strategies of teachers in line with managers’ intended learning-path strategies for teachers?||x||Re-analysis of 18||2||Employability Professional-driven|
|21||Witteveen (2013)||What are the motives of teachers to invest in their professional development?||x||Re-analysis of 18|
|22||Franken (2015)||What is the relationship between the dominant actor and the level of reflection within learning paths?||Radiologists;
Managers of medical device company
|109||x||Re-analysis of Yates (2011) and Watkins et al. (2011)|
|23||Kooistra (2015)||How do teachers operate strategically in linking their learning motives to the way they create learning-paths?||Teachers||x||Re-analysis of 17, 18 and 19||1||Challenge seeker|
* Indicates that a thesis was written in Dutch
Appendix 3. Coding scheme for learning path(s) (strategies)
Learning motives (M): learning for […]
M1: […] career development (for promotion, for career, for salary, for broadening tasks).
M2: […] professional development (for performing well, for developing the profession, for developing expertise).
M3: […] social functioning (for keeping up with colleagues, for avoiding dismissal, for supervisory appreciation, for the team spirit).
M4: other […].
Learning themes (T): learning in the […]
T1: […] technical-practical domain (general skills, professional skills, knowledge, physical coping).
T2: […] socio-emotional domain (contact with clients, daring to communicate, appearance, psychological coping, proactive attitude).
T3: […] organizational domain (task-management skills, coordinating tasks, role and environment skills).
T4: […] developmental domain (learning and collecting information, self-knowledge)
T5: other […].
Learning activities (a): learning from […]
A1: […] experience.
A2: […] social interaction.
A3: […] theory (formal settings).
A4: […] theory (self-directed).
A5: […] reflection.
A6: […] teaching others.
A7: […] one’s private life.
A8: other […].
Social learning context (C): learning with […]
C1: […] colleagues.
C2: […] supervisors.
C3: […] experts.
C4: […] educators, trainers, coaches.
C5: […] clients, customers.
C6: […] external persons.
C7: […] persons from the private life of the respondents.
C8: other […].
Learning facilities (F): learning by receiving and using […]
F1: […] social support.
F2: […] formal training/education/development opportunities.
F3: […] material learning facilities.
F4: […] financial means.
F5: […] available time.
F6: other […].
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