The influence of personality traits on engagement in lifelong learning

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International Journal of Lifelong Education
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The influence of personality traits on engagement
in lifelong learning
Daniel Eriksson Sörman, Elisabeth Åström, Mikael Ahlström, Rolf Adolfsson
& Jessica Körning Ljungberg
To cite this article: Daniel Eriksson Sörman, Elisabeth Åström, Mikael Ahlström, Rolf
Adolfsson & Jessica Körning Ljungberg (18 Apr 2024): The influence of personality traits
on engagement in lifelong learning, International Journal of Lifelong Education, DOI:
10.1080/02601370.2024.2343013
To link to this article: https://doi.org/10.1080/02601370.2024.2343013
© 2024 The Author(s). Published by Informa
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Group.
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The inuence of personality traits on engagement in lifelong
learning
Daniel Eriksson Sörman
a,b
, Elisabeth Åström
b
, Mikael Ahlström
a
, Rolf Adolfsson
c
and Jessica Körning Ljungberg
a
a
Department of Health, Education, and Technology, Luleå University of Technology, Luleå, Sweden;
b
Department of
Psychology, Umeå University, Umeå, Sweden;
c
Department of Clinical Science, Umeå University, Umeå, Sweden
ABSTRACT
Today, adult individuals must be able to continuously learn and adapt to
the rapid changes occurring in society. However, little is known about the
individual characteristics, particularly personality traits, that make adults
more likely to engage in learning activities. Moreover, few studies have
longitudinally and objectively investigated the inuence of personality on
engagement in lifelong learning throughout working age. This study
therefore used longitudinal data (15 years) to examine which personality
traits predicted level and long-term changes in learning activities among
1329 Swedish adults aged 30–60. The results from growth curve model-
ling showed that over the follow-up period, novelty seeking and self-
transcendence were both positively related to overall level of engage-
ment in learning activities, although not to rate of change. Regarding
specic activities, novelty seeking was related to higher levels of engage-
ment in attending courses, taking on new education, and making occupa-
tional changes, while harm avoidance was negatively related to the
likelihood of changing occupation. The results of this study underscore
the importance of considering personality in relation to engagement in
lifelong learning activities. Insights from this study can potentially increase
the likelihood of nding methods to promote lifelong learning, which can
be benecial for educators, policymakers, and companies.
ARTICLE HISTORY
Received 22 September 2023
Accepted 9 April 2024
KEYWORDS
Lifelong learning;
personality; working age
Introduction
Given the demographic changes happening worldwide, along with the fast development of knowl-
edge, new innovations, and technological changes, it is crucial for individuals to constantly adapt to
their evolving societies. In addition, and partly as a consequence of the rapid development of
society, the labour market continuously puts new demands on the individual (see e.g. Kaplan, 2016;
Tuckett & Field, 2016). However, it is not given that everyone has the will or motivation to
continuously learn and change throughout life. Therefore, an increased understanding is required
of the factors that motivate individuals to acquire new knowledge over their lifespan and working
life. It has been suggested that factors such as age, educational level, socioeconomic status, parents’
educational background, expectations of family, development opportunities, employment pro-
spects, higher income, social benefits, health issues, and expectations from the workplace may
CONTACT Daniel Eriksson Sörman daniel.sorman@umu.se Department of Psychology, Umeå University, Umeå SE-901
87, Sweden
Supplemental data for this article can be accessed online at https://doi.org/10.1080/02601370.2024.2343013
INTERNATIONAL JOURNAL OF LIFELONG EDUCATION
https://doi.org/10.1080/02601370.2024.2343013
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.
0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which
this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

influence the likelihood of engaging in learning activities (see e.g. Boudard & Rubenson, 2003;
Gorard & Selwyn, 2005; Maclean et al., 2013; Macleod & Lambe, 2007; White, 2012).
However, relatively little is known about how individual characteristics, and personality traits in
particular, may influence an adult’s engagement (level and change) in learning activities by
following behavioural patterns across time. Furthermore, as noted by Laible et al. (2020), most
studies in this area have focused on predictors of work-related training activities rather than non-
work-related learning activities. A deeper understanding of how personality can influence engage-
ment in learning activities over a working life, and perhaps also help the individual to maintain
occupational and intellectual development, may be an important clue in understanding why some
individuals are more likely than others to be able to adapt to societal change.
The concept of lifelong learning
In the Hamburg declaration (UNESCO, 1997) it is claimed that adult education, conceived within
the framework of lifelong learning, should be looked upon as a lifelong process that will enable both
individuals and society to handle future challenges. The question then is how to understand the
concept of lifelong learning. The Commission of the European Communities (2001) has suggested
that lifelong learning can be seen from a personal, social, civic, and employment perspective, and
that it includes all learning activity undertaken over life to reflect how individuals gain knowledge,
skills, and competencies. Lifelong learning has also been regarded as purposeful and ongoing
acquisition of skills and knowledge throughout life (Longworth, 2006). Another relatively broad
description used in the literature is that lifelong learning includes both formal and informal
education that may help the individual to acquire knowledge that was lacking from previous formal
education (Hus, 2011). Moreover, and of relevance to the current study, lifelong learning can be
considered as the voluntary and self-motivated pursuit of knowledge, and as a type of learning that
can be executed for both personal and professional reasons (Ates & Alsal, 2012). When considering
such aspects of engagement in lifelong learning activities, and noting that to a large extent learning
is driven by the interest and motivation of the individual, it seems plausible that engagement in
learning may be differently influenced by personality characteristics or traits as compared to when
engagement in activities is not fully driven by the individual’s own interest or motivation.
Although many actions can be related to the acquisition of knowledge, there is no consensus in
the literature regarding what specific activities may fit within the framework of lifelong learning, and
so the concept may include any activity ‘organised with the intention to improve an individual’s
knowledge, skills and competences’ (Eurostat, 2016, p. 10). However, learning activities are often
divided into formal, non-formal, and informal learning, with the first two of these referring to
activities that are often organised, such as in a classroom. Formal learning activities often include an
accepted degree or certified education. Non-formal education, on the other hand, often happens
outside the formal educational curriculum but still within an organisational framework. Informal
learning is often less structured, spontaneous, and unintentional. Such learning takes place outside
school with, for instance, family and friends, or when reading literature. It may also take place
spontaneously within the context of work (Marsick & Volpe, 1999). It should be noted, however,
that others have suggested that two criteria must be fulfilled for activity to be defined as a learning
activity, as compared to a non-learning activity. One is that the learning activity must be intentional
with a purpose (not random), and organised in some way; the other is that it should include the
transfer of information through, for instance, messages, ideas, knowledge, or strategies. Moreover,
the intention of learning must be expressed before the activity starts (Eurostat, 2016).
The concept of personality
As previously noted, relatively few studies have investigated how individual characteristics such as
personality traits influence engagement in learning activities over working life. Personality traits are
2D. E. SÖRMAN ET AL.

indicative of relatively stable patterns of behaviours, thoughts, and feelings in an individual (Roberts
et al., 2008). Within the trait perspective of personality, there exist several conceptual models.
Common to all is that personality is described as a collection of traits that can be more or less
adaptive. One popular model is the five-factor model of personality, which describes personality
according to five core dimensions or traits (Digman, 1990; Goldberg, 1993). Another example is
Cloninger’s psychobiological model of personality (Cloninger et al., 1993), which guided research in
the current study. Although Cloninger’s model and the five-factor model share similarities, con-
sidering that they are both trait-based and aim to provide a comprehensive overview of personality,
they also have fundamental differences. According to Cloninger’s model, personality consists of
four temperament traits (conceived to be genetically based and relatively stable) and three character
traits (conceived to be shaped by experience and social learning and thus more susceptible to
change). The notion is that personality can be understood from a perspective of interactions
between temperament and character. Thus, whereas Cloninger’s model distinguishes between
inherited temperament traits and character traits shaped by the environment, the five-factor
model does not distinguish between inherited and learned traits. Cloninger’s model, measured by
the Temperament and Character Inventory (TCI, Cloninger et al., 1993), also has a more complex
structure than the five-factor model because it has a double emphasis on temperament and
character. Despite these differences, there appears to be an overlap between the traits within the
five-factor model and Cloninger’s model (e.g. Aluja & Blanch, 2011; Capanna et al., 2012;
MacDonald & Holland, 2002). For example, both Capanna et al. (2012) and MacDonald and
Holland (2002) found significant correlations between one or more of the traits in Cloninger’s
model and the traits within the five-factor model. Nonetheless, both instruments are commonly
used in psychological research (Feher & Vernon, 2021).
Personality and lifelong learning
Today, it is well-established that personality traits can predict educational level (Prevo & Ter Weel,
2015), academic performance (Andersen et al., 2020), job search activities (Caliendo et al., 2010)
and occupational success (Furnham, 2018). Regarding engagement in adult learning activities,
particularly in further education and training, personality traits have been found to influence
attitudes towards learning (Fouarge et al., 2012). However, it is important to distinguish between
attitudes and actual engagement in learning activities.
Regarding research on personality and actual engagement in learning activities over working life,
the literature is relatively sparse. In one of the studies that constitute the exception, Offerhaus
(2013) used longitudinal data to investigate the relationship between personality and participation
in employment-related further education and training. The study, which collected data at three
points over a nine-year period from a large sample, utilised the five-factor model of personality and
locus of control as personality indicators. The results showed that individuals with high scores on
the personality factor of openness, which indicates a tendency to engage in new experiences,
curiosity, and an interest in exploring their inner life (Costa & McCrae, 1992), as well as those
with high internal control beliefs, meaning a belief that they can influence their lives and outcomes,
are more inclined to participate in further education and training related to employment. The
personality factor conscientiousness, which describes an individual’s ability to organise, sense of
responsibility, diligence, and goal orientation, was also found to have an impact, especially in its
most extreme forms where it decreased the chances of participation in employment-related further
education and training. However, the other factors in the five factor model, namely agreeableness,
extraversion, and neuroticism, had no impact on participation in employment-related further
education and training. The results from this study are intriguing and offer insight into how
personality influences engagement in lifelong learning. What still needs to be explored, though, is
how personality influences adult learning when learning activities are not explicitly employment
related.
INTERNATIONAL JOURNAL OF LIFELONG EDUCATION 3

In another of the few studies that have addressed the relationship between personality
and lifelong learning from a longitudinal perspective, Laible et al. (2020) used objective
measures from the German National Educational Panel study to examine whether the
personality factors of the five-factor model could be related to engagement in lifelong
learning. The participants were aged between 25 and 65, and were followed over a period
of approximately three years. Overall, the results indicated that two personality factors,
‘extraversion’ and ‘openness to experience’, were significantly and positively related to the
probability of engaging in lifelong learning, which included both non-formal and informal
learning activities. It therefore seems that individuals who are more outgoing and energetic
(extraversion) or open to unusual ideas and adventures (openness to experience) are also
more likely to engage in learning activities during working life. For instance, it was found
that openness to experience could predict engagement in competence development. It
should nevertheless be stressed that openness had a somewhat stronger association with
informal activities than with non-formal activities. The results also revealed that the
personality factor ‘agreeableness’ (the capacity to put others’ needs before one’s own) was
related to non-formal learning activities. Overall, these findings indicate that personality
traits may need to be considered when investigating what makes an individual more likely
to engage in learning activities over time.
However, to the best of the present authors’ knowledge, no studies have objectively investigated
how individual characteristics such as personality traits may influence an individual’s engagement
in learning activities over a longer time frame. Correspondingly, there is also a lack of studies that
have investigated personality in relation to lifelong learning by using indicators of actual engage-
ment in learning activities over time, rather than evaluating an individual’s subjective willingness or
likelihood to engage in lifelong learning.
The present study
In the current study, we included three activities related to lifelong learning that were undertaken
voluntarily and by an individual’s own initiative (in contrast to, for example, mandatory skills
development within a workplace): ‘attending courses’, ‘education’, and ‘occupational changes’.
Courses often include a series of fixed classes to promote learning on a topic, whereas education,
according to a Swedish definition, can refer to systematic teaching and training that provides
knowledge and skills for a particular occupation and often provides some formal competence for
this (Svensk ordbok, 2021). In this study, occupational change should be understood from
a perspective of changing occupational category, and not be confused with a change of workplace.
As already noted, the fact that activities are undertaken voluntarily by the individual has been
deemed important in relation to the motivation to engage in lifelong learning (Ates & Alsal, 2012).
The activities included in this present study may support the individual to gain knowledge lacking
from previous formal education (Hus, 2011). They also fit into the common definition that lifelong
learning reflects purposeful learning that is undertaken throughout life, that aims to improve
knowledge, skills, and competencies, and that can be seen from a personal, social, civic, or employ-
ment perspective (Commission of the European Communities, 2000). Two of them, attending
courses and education, can be considered organised forms of learning activities, which can be either
formal or non-formal. They can both be defined as activities with the purpose of learning, they both
include transfer of information, and in both cases the intention of learning will most likely have
been expressed before the activities started. The third learning activity included in this study,
occupational change, is not a formal learning activity in the sense of being an organised form of
learning, and it may not totally fulfil the criterion of learning as an activity that needs to be
communicated before the activity starts (Eurostat, 2016). However, as noted above, changing
occupation fits within the definition of lifelong learning as suggested by, for instance, Tuckett
and Field (2016) and the Commission of the European Communities (2000). Moreover, and again
4D. E. SÖRMAN ET AL.

as noted previously, both formal and informal learning activities can constitute a part of lifelong
learning.
More substantiated research is needed to help us understand how to respond to the needs and
fast changes taking place in today’s society, in terms of rapid transitions, engagement in further
education, and taking on new professional roles. In this study, we used unique longitudinal data to
examine whether individual differences in personality could influence motivation to continuously
learn over a working life.
Aims
The overall aim of the present study was to investigate how personality traits predict an adult’s level
of and long-term change in learning activities over their working life. More specifically, we aimed to
investigate how personality traits novelty seeking, harm avoidance, reward dependence, persistence,
self-directedness, cooperativeness, and self-transcendence were predictive of level and change for
engagement in learning activities (courses, education, change of occupation). The study included
data from baseline and from follow-ups at 5, 10, and 15 years. The potential influence of factors
such as age, gender, years of education, occupational complexity, and indicators of socioeconomic
status (SES) were also considered in the analyses. Results from this study will expand our under-
standing of how personality influences engagement in lifelong learning. By including learning
activities reflective of various aspects of lifelong learning this study will contribute to a broader
understanding of how personality affects adult learning. The longitudinal design and the use of
a population-based sample will also offer unique insights into how the influence of personality on
engagement in learning activities manifests both in the long and short term in an adult population
still in working life. The outcome from this study may be of value for political decision making,
companies, and educators when aiming to enable lifelong learning. The results could also poten-
tially help individuals to understand the underlying mechanisms behind their own motivation (or
lack thereof) to engage in various learning activities.
Materials and methods
Study population
The data used in this study were drawn from the Betula prospective cohort study (Nilsson et al.,
1997, 2004; Nyberg et al., 2020), which is a Swedish longitudinal study of ageing, memory, and
health. The participants, selected by stratified (age, gender) random sampling from the population
registry, have been tested at 5-year intervals since 1988, covering six test waves: 1988–1990, 1993–
1995, 1998–2000, 2003–2005, 2008–2010, and 2013–2014. The Betula study has been shown to be
representative in terms of population validity, considering factors such as education level, gender,
marital status, income, and household size (Nilsson et al., 1997). Participation in this study includes
extensive health assessment and cognitive testing. These assessments are separated into two
sessions, with one focusing on health and the other mostly on cognitive functioning. At each test
wave, these sessions are separated by about one week. Due to the number of participants to be
tested, and the large amount of data to be collected from each participant, it has taken approxi-
mately two years to complete all testing in each test wave. However, in the Betula study, it has been
carefully managed to ensure that each participant had approximately a 5-year interval between their
individual testing sessions across the test waves. The test wave that was conducted between years
1993–1995 was used as the study baseline, Time 1 (T1), in this present study to allow the inclusion
of repeated measures of data that were relevant to this study and that were equally measured over
time. Three of the samples (sample 1, sample 2, and sample 3), each including different randomly
selected individuals from the population, contribute with longitudinal data and hence were used in
the present study (other samples have been included only in one test wave, primarily to control for
INTERNATIONAL JOURNAL OF LIFELONG EDUCATION 5

potential learning and cohort effects). Sample 1 contributed with data over four test waves (1993–
1995, 1998–2000, 2003–2005, and 2008–2010), sample 2 over two test waves (1993–1995, and 1998–
2000), and sample 3 over four test waves (1993–1995, 1998–2000, 2003–2005, and 2008–2010). Data
collected between 2013 and 2014 were not included in the present study because the recruitment
procedure was different, also resulting in less available data on the key variables of interest. Thus,
the data used in the present study included information from four measurement points: T1 (1993–
1995), T2 (1998–2000), T3 (2003–2005), and T4 (2008–2010), equivalent to a follow-up period of
15 years. We included participants who were aged between 35 and 60 at the study baseline and set
the end age of follow-up at 65 years (the common retirement age in Sweden) in order to be able to
follow participants when they were still of working age.
All participants gave written informed consent in accordance with the Declaration of Helsinki.
The study was approved by the Regional Ethical Review Board in Umeå (approvals no. 870303, 97–
173, 221/97, 97–173, 03–484, 01–008, 169/02, 02–164, 03–484, 05–082 M, and 08-132 M).
Participants
At baseline (T1), the study included 1600 participants aged 35–60 years from samples 1 (n =
500), 2 (n = 600), and 3 (n = 500). Of the 1343 who responded to the questionnaire (TCI) about
their personality, 14 had to be excluded due to missing data on engagement in learning
activities at baseline, and so the final baseline sample consisted of 1329 participants (sample
1 = 394, sample 2 = 486, sample 3 = 449). Data were available for 1081 participants (sample 1 =
363, sample 2 = 298, sample 3 = 420) at the 5-year follow-up, for 584 participants (sample 1 =
268, sample 3 = 316) at the 10-year follow-up, and for 362 participants (sample 1 = 167, sample
3 = 195) at the 15-year follow-up. At baseline, the study sample had a mean age of 49.29 years
(SD = 7.68, range 35–60 years) and comprised 52.4% women. It should be noted that the
significant differences in sample size over time were largely due to the study design (i.e.
Sample 2 did not contribute data for the entire follow-up period) and exclusion criteria (such
as retirement age). The main reason for including all samples with longitudinal data was to
enhance statistical power and reliability of the outcomes.
Measures
Personality
The Temperament and Character Inventory (TCI; Cloninger et al., 1993) is the instrument used
in the Betula study for personality assessment. This paper-and-pencil-test of TCI is a validated
instrument that includes 238 statements (true/false), and we coded and treated the data
according to standard procedures. In this inventory, personality is explored based on seven
dimensions (subscales) which can further be summarised to represent one of two overarching
factors: temperament (novelty seeking, harm avoidance, reward dependence, persistence) or
character (self-directedness, cooperativeness, self-transcendence). Novelty seeking (based on 40
items) is a heritable temperament trait that reflects a bias towards activation or initiation of
actions and towards frequently exploring new things as a response to novelty. This personality
dimension also includes impulsive decision making, quick loss of temper, avoidance of frustra-
tion, and excessive bias to approach cues for potential rewards. Harm avoidance (based on 35
items) reflects the individual’s bias towards inhibiting behaviours, pessimistic worry about future
problems, doubtfulness, fear of uncertainty, shyness, and being easily fatigued. Reward depen-
dence (based on 24 items) reflects the tendency to preserve and continue to engage in ongoing
behaviours associated with reward or relief of punishment, to react strongly to reinforcement
and the approval of others, and to depend on social attachment. Persistence (based on 8 items)
is the propensity for perseverance even in the presence of fatigue or frustration. Among the
character traits, self-directedness (based on 44 items) is related to how responsible, dutiful, and
6D. E. SÖRMAN ET AL.

self-accepting the individual is. Cooperativeness (based on 42 items) concerns the extent of
agreeableness in relations with others, and is related to the degree to which the individual
identifies with and accepts others. Self-transcendence (based on 33 items) reflects personal
boundaries; for instance, how the individual considers themself as a part of the universe.
Lifelong learning
Indicators of lifelong learning including attending courses, engagement in new education, and
occupational change were measured with the Life Event Inventory (Perris, 1984), which was
administered at each test wave (T2–T5) used in the present study. The participants indicated
whether any of the events had occurred during the last five years (i.e. between test waves), and if
so, whether they felt that this event was something that could be influenced by the individual. Thus,
participants only received a score (yes = 1, no = 0) for engagement in an activity that they them-
selves had actively influenced.
Confounders
Potential confounders included in the analyses were age, gender (1 = female, 0 = male), years of
education, and occupational complexity (for a detailed description, see Sörman et al., 2021). Two
self-reported living conditions were included as indicators of SES: number of rooms (excluding the
kitchen), and number of persons in the household.
Statistical analysis
Descriptive statistics (means, standard deviations, skewness, kurtosis) were calculated for all
the variables included in this study, and correlational analyses were employed between
variables. The percentage distribution for engagement in learning activities over the test
waves was also calculated. Next, latent growth curve modelling was executed. Models were
analysed with SPSS-28 and AMOS-28 using maximum likelihood estimation, and the
significance level was set to 0.05. First, an unconditional model (i.e. without predictor
variables) including four time points of lifelong learning data (baseline, 5 years, 10 years,
and 15 years) was tested to gain information about mean changes, variance in level (i.e. the
intercept) and change (i.e. the slope), and the extent to which intercept and slope were
associated when predictor variables were not considered. Three measures (attending
courses, new education, and occupational change) were used as indicators of a latent
lifelong learning construct across time. After this, the conditional model was tested. In
this second-order latent growth model, the intercept and slope were conditioned by pre-
dictor variables, and personality factors and confounders were thus included in the model.
Both growth curve models (unconditional and conditional) were evaluated with the χ2
statistic divided by its degrees of freedom (df), the Tucker – Lewis index (TLI), the
comparative fit index (CFI), and the root mean square error of approximation (RMSEA)
with a 90% confidence interval. Suggested cut-off criteria according to Kline (2010) and
Hooper et al. (2008) were used to estimate adequate model fit (TLI > 0.90, CFI > 0.90,
RMSEA < 0.08) and good model fit (TLI >0.95, CFI > 0.95, RMSEA < 0.06).
Results
Participant characteristics, measures of probability distribution, and intercorrelations between
study variables are given in Table 1, and the percentage distribution for engagement in learning
activities over the test waves is given in Table 2.
Most of the variables included in this study were normally distributed. The only variables with
a value that might be slightly above the upper threshold suggested in the literature were learning
activity at T3 and learning activity at T4; however, this was extremely marginal, with skewness
INTERNATIONAL JOURNAL OF LIFELONG EDUCATION 7

Table 1. Means (M), standard deviations (SD), skewness, kurtosis, and intercorrelations between study variables.
Variable M SD % Skewness Kurtosis 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1. Age 49.29 7.68 −0.12 −1.14 -
2. Female gender 52.4 .040 -
3. Years of education 12.16 3.81 0.33 −0.27 −.382** .004 -
4. Occupational
complexity
32.98 6.37 −0.05 −0.51 −.063* −.185** .478** -
5. SES (number of rooms) 4.49 1.39 0.15 0.24 −.096** −.045 .211** .202** -
6. SES (number of
persons in household)
2.75 1.27 0.66 0.10 −.529** −.095** .268** .118** .498** -
Temperament
7. Novelty seeking 19.19 5.63 0.06 −0.14 −.232** .042 .229** .131** .046 .102** -
8. Harm avoidance 13.41 6.17 0.43 −0.20 .059* .235** −.047 −.149** −.117** −.186** −.257** -
9. Reward dependence 15.22 3.67 −0.24 −0.22 −.047 .337** .016 −.060* .040 .039 .114** .101** -
10. Persistence 3.99 1.85 0.11 −0.63 −.117** −.040 .090** −.114** .092** .104** −.037 −.148** .047 -
Character
11. Self-directedness 33.33 5.80 −0.86 0.69 .090** −.025 .013 .112** .069* .002 −.017 −.466** .029 −.043 -
12. Cooperativeness 34.36 4.45 −1.22 2.04 .038 .172** .001 .013 .047 .026 .002 −.148** .390** .070* .303** -
13. Self-transcendence 12.26 5.98 0.60 −0.11 .069* 207** −.026 −.016 −.032 −.037 .162** −.126** .220** .204** −.004 .222** -
Lifelong learning
14. Learning activity at
T1
a
0.47 0.67 1.28 1.16 −.274** .063* .283** .141** .068* .168** .205** −.066* .047 .087** .027 .022 .065* -
15. Learning activity at
T2
a
0.32 0.57 1.71 2.53 −.323** .041 .222** .101** .106** .191** .184** −.077* .070* .076* −.005 −.004 .083* .280** -
16. Learning activity at
T3
a
0.28 0.55 2.03 4.06 −.273** .095* .144** .037 .020 .140** .143** −.039 .067 .074 −.072 .045 .085* .163** .364** -
17. Learning activity at
T4
a
0.22 0.49 2.37 5.66 −.223** .065 .171** .044 −.015 .056 .072 −.023 .002 .083 −.031 .031 .093 .136** .187** .239** -
Note: a = Composite score of learning activities.
*p < .05, **p < .01 (two tailed).
8D. E. SÖRMAN ET AL.

values of 2.03 and 2.37, respectively. One suggested upper threshold for skewness is 2.0 (see e.g.
Finney & DiStefano, 2006).
Although participants got increasingly older and the sample size became significantly smaller
over time, sample characteristics remained relatively stable over the test waves. There was, however,
a small tendency that those that were more highly educated, had higher level of occupational
complexity, and more persons in the household to a higher extent contributed with data over time.
This is most likely reflective of cohort effects (i.e. participants younger at the start of the project had
more years of education, higher occupational complexity, and more people lived in their house-
hold). Sample characteristics at each wave of the study can be found in the supplementary material,
Table S1.
Results from the correlational analyses are presented in Table 1. Overall, age showed a significant
correlation with learning activities over all test waves (
r
range: −.22 to −.32), indicating that older
individuals were less likely to engage in such factors. More years of education also had a significant
positive correlation with learning activities in all test waves (
r
range = .14 to .28). Being female was
positively related to learning activities in test waves one (r = .06) and three (r = .10). Occupational
complexity was significantly associated with learning activities at the first (r = .14) and second test
wave (r = .10). Regarding SES, number of rooms in household was related to learning activities at the
first two test waves (r = .07 and r = .11), whereas number of persons in household was significantly and
positively related to learning activities over the first three test waves (
r
range: .14 to .19)
Among the personality factors, novelty seeking was significantly and positively associated with
learning activities over the first three test waves (
r
range: .14 to .21), harm avoidance was negatively
associated with learning activities over the first two test waves (r = −.07 and r =−.08), and reward
dependence was positively correlated with learning activity at the second test wave (r = .07).
Persistence was associated with learning activity at the first (r = .09) and second (r = .08) test
waves, and self-transcendence was positively related to learning activities at the first three test waves
(
r
range: .07 to .09).
Next, the fit of the unconditional growth model was tested. This model, including the four time
points of longitudinal data (i.e. learning activities) with no predictor variables included, revealed
adequate model fit according to TLI (= .926) and good model fit according to CFI (= .967), RMSEA
(= .017, 90% CI [00–03]), and chi-square/df ( = 1.37, p = .071). All indicators of learning activity
loaded significantly on the relevant latent factor across all time points (p = < .05). The intercept and
slope showed a negative correlation (r = −.66, p < .001). Moreover, means and variances for both
intercept (M = .113, S.E. = .008, C.R. = 14.307, p = < .001; S
2
= .006, S.E. = .002, C.R. = 2.878, p
= .004) and slope (M = −.004, S.E. = .001, C.R. = −3.786, p = < .001, S
2
= .000, S.E. = .000, C.R. =
2.094, p = .036) were significant. Thus, the results indicated significant between-person differences
regarding both level and change in lifelong learning, and so continued analyses including predictor
variables (conditional model) were appropriate.
Results from the conditional growth curve model revealed good model fit according to all fit
indices: TLI (= .958), CFI (= .979), RMSEA (= .020; 90% CI [.01–.02]), and chi-square/df ( = 1.51, p
= <.001). Standardised and unstandardised estimates for the predictor variables on lifelong learning
(intercept and slope) in the model are presented in Table 3 along with standard errors, critical
ratios, and p values.
Table 2. Percentage distribution for engagement in learning activities over the 15-year follow-up
period including four test waves (T1–T4), each separated by five years.
T1 (%)
n = 1329
T2 (%)
n = 1081
T3 (%)
n = 584
T4 (%)
n = 362
No activity 61.4 72.7 76.5 81.5
One activity 30.7 22.8 19.3 15.5
Two activities 6.9 4.1 3.6 2.8
Three activities 1.0 0.4 0.5 0.3
INTERNATIONAL JOURNAL OF LIFELONG EDUCATION 9

Among the personality factors, novelty seeking was positively associated with the intercept for
lifelong learning, but also had a borderline (p = .070) negative association with slope. These findings
suggest that individuals with higher levels of novelty seeking are more likely to engage in learning
activities, but the trend in the data may indicate that this effect does not hold longitudinally, as
shown by less interest in lifelong learning over time. Self-transcendence was also positively and
significantly related to the intercept, suggesting that higher levels of this personality trait were
associated with higher levels of engagement in learning activities, but it was not related to change
over time. None of the other personality traits were associated with intercept or slope in lifelong
learning.
Among the other predictor variables, age was significantly and negatively associated with
intercept, suggesting that higher age is related to lower levels of engagement in learning
activities. However, higher age was not related to slope. Thus, even if there is a negative
change in engagement in lifelong learning over time (as indicated by changes in mean
levels), which consequently is part of the ageing trajectory, age does not predict differences
in change between individuals over time. A higher number of years of education was
positively related to level of engagement in learning activities, but was not related to
change. Finally, none of the SES indicators, gender, or occupational complexity were
associated with intercept or slope of lifelong learning. It should be noted that gender had
a borderline relationship (p = .065) with intercept, indicating that women on average might
be more likely to engage in learning activities.
Table 3. Summary of the results from the conditional latent growth model including predictors of level and change in lifelong
learning.
Lifelong learning
β b SE CR p
Age → I −.445 −.005 .001 −5.766 <.001
Age → S −.266 .000 .000 −1.468 .142
Gender → I .107 .020 .011 1.842 .065
Gender → S −.018 −.000 .001 −0.147 .883
Education → I .261 .006 .002 3.863 <.001
Education → S −.124 .000 .000 −0.865 .387
Occupational complexity → I .047 .001 .001 0.766 .443
Occupational complexity → S −.050 .000 .000 −0.387 .699
Number of rooms → I .004 .000 .004 0.066 .947
Number of rooms → S −.073 .000 .000 −0.387 .699
Number of persons in household → I .058 .004 .005 0.824 .410
Number of persons in household → S −.134 −.001 .001 −0.888 .374
Novelty seeking → I .240 .004 .001 4.015 <.001
Novelty seeking → S −.227 .000 .000 −1.809 .070
Harm avoidance → I −.025 .000 .001 −0.387 .699
Harm avoidance → S −.083 .000 .000 −0.591 .554
Reward dependence → I .028 .001 .001 0.479 .632
Reward dependence → S −.047 .000 .000 −0.374 .708
Persistence → I .065 .003 .003 1.206 .228
Persistence → S −.021 .000 .000 −0.181 .857
Self-directedness → I .096 .002 .001 1.555 .120
Self-directedness → S −.232 .000 .000 −1.749 .080
Cooperativeness → I −.051 −.001 .001 −0.864 .387
Cooperativeness → S .125 .000 .000 0.990 .322
Self-transcendence → I .119 .002 .001 2.103 .035
Self-transcendence → S −.015 .000 .000 −0.122 .903
I = intercept, S = slope, β = standardised regression weight, b = unstandardised regression weight, SE = standard error, CR =
critical ratio.
10 D. E. SÖRMAN ET AL.

Additional analyses
The number of individuals in the sample who had engaged in a specific activity (courses, education,
occupational change) at each test wave was overall too low to be able to reliably investigate effects of
personality on each activity at each measurement point. This issue also affected the possibility of
investigating what can cause change in each learning activity over time. For this reason, it was
a correct choice to examine the total amount of learning activities at each measurement point via
a latent construct.
However, additional linear regression analyses were executed to gain some clarity regarding the
role of personality traits as predictors of engagement in different learning activities. In these
analyses, indexes were constructed for each learning activity, based on the number of times that
the individual had engaged in each learning activity over the entire follow-up period divided by the
number of measurement points at which that the participant had contributed data. The use of
indexes to represent the total follow-up period cannot provide information about the extent to
which independent variables can predict change in learning activities over time. However, it does
provide some information about the amount of engagement in specific learning activities when the
total follow-up period is considered. The results from these additional analyses are presented in
Table 4.
Novelty seeking was significantly and positively related to all learning activities, con-
firming the findings from the growth model. Harm avoidance was related to being less
willing to change occupation. Self-transcendence, which was related to learning in the
growth model, did not have enough impact to significantly influence any individual learn-
ing activity, although it should be noted that it had a borderline relationship with occupa-
tional change (p = .053). Among the confounders, age was negatively related and number of
years of education was positively related to all learning activities, again confirming the
findings from the growth model. However, analyses of gender revealed a somewhat different
pattern; being female was related to being more engaged in courses, but also to being less
willing to change occupation. Finally, higher levels of occupational complexity were related
to higher levels of attending courses, and the SES factor ‘number of rooms in household’
was related to less engagement in new education.
Table 4. Results from linear regression analyses with different learning activities as dependent variables. Involvement in each
learning activity is calculated and weighted based on the amount considering the entire follow-up period (up to 15 years).
Attending courses New education Occupational changes
Predictor β b SE p β b SE p β b SE p
Age −.144 −.006 .001 <.001 −.139 −.003 .001 <.001 −.235 −.007 .001 <.001
Gender .117 .074 .019 <.001 .052 .017 .010 .091 −.067 −.031 .014 .025
Years of education .164 .014 .003 <.001 .129 .006 .001 <.001 .008 .000 .002 .814
Occupational complexity .080 .004 .002 .011 −.026 −.001 .001 .428 −.005 .000 .001 .867
SES (number of rooms) .049 .011 .007 .129 −.071 −.008 .004 .031 −.048 −.008 .005 .132
SES (number of persons in
household)
−.059 −.015 .009 .109 .069 .009 .005 .065 .068 .012 .007 .065
Novelty seeking .080 .004 .002 .007 .068 .002 .001 .026 .096 .004 .001 .001
Harm avoidance .032 .002 .002 .340 .013 .000 .001 .704 −.083 −.003 .001 .014
Reward dependence −.005 .000 .003 .864 −.017 −.001 .001 .580 .015 .001 .002 .619
Persistence .049 .008 .005 .085 −.023 −.002 .003 .422 .038 .005 .004 .179
Self-directedness .038 .002 .002 .238 .042 .001 .001 .200 .014 .001 .001 .656
Cooperativeness −.008 −.001 .002 .787 −.022 −.001 .001 .494 .019 .001 .002 .532
Self-transcendence .042 .002 .002 .148 .040 .001 .001 .182 .056 .002 .001 .053
Total R2 = .117 .076 .128
β = standardised regression weight, b = unstandardised regression weight, SE = standard error.
INTERNATIONAL JOURNAL OF LIFELONG EDUCATION 11

Discussion
Summary of key ndings
This study examined level and long-term change in lifelong learning in relation to personality
factors in a sample of working-age individuals. The results indicate that two personality
dimensions in particular were related to level of engagement in learning activities: novelty
seeking and self-transcendence. However, these factors were not significantly related to change
in lifelong learning over the 15-year follow-up period, although it should be noted that novelty
seeking was borderline significant for slope in the growth curve model. Additional analyses,
investigating the influence of personality traits on level of involvement in specific activities
taking the total follow-up period into account, revealed that novelty seeking was related to
higher engagement in all activities included in this study (courses, education, occupational
change). Harm avoidance was negatively related to the likelihood of changing occupation, and
self-transcendence, which was positively related to learning activities in the latent growth model,
was borderline significant for change of occupation. No other personality trait (reward depen-
dence, persistence, self-directness, cooperativeness) was associated with lifelong learning as
measured in this study.
Interpreting key ndings of this study: personality and lifelong learning
The finding that novelty seeking was related to overall engagement in learning activities, as well as
to participation in specific learning activities, seems reasonable. This trait is very much heritable,
and refers to a tendency towards sensation seeking and the pursuit of new experiences (Cloninger
et al., 1993). Results from previous studies have, for instance, shown that novelty seeking is
associated with the activation of dopaminergic pathways (Wiesbeck et al., 1995). The motivation
to engage in new activities may therefore be supported and modulated by the fact that novel stimuli
can excite dopamine neurons and trigger brain regions with dopaminergic input (V. D. Costa et al.,
2014). Although it has been found that individuals with high novelty seeking may be at higher risk
of negative health behaviours such as drug and alcohol addiction (Grucza et al., 2006), the results
from this study conversely suggest that this personality trait may be adaptive with regard to the
tendency and willingness to engage in new learning activities and adapt to evolving societies.
On the other hand, we cannot rule out the possibility that individuals with high novelty seeking
will also have difficulty engaging in specific activities over longer time periods. It may be that such
individuals constantly seek novelty, and thus tend to leave ‘old’ activities behind in the search for
new activities that will feed their curiosity or help them avoid boredom (Liang et al., 2020). In the
present study, we were not able to investigate change in any specific activities. However, novelty
seeking was borderline significant for change (negative slope) in overall engagement in lifelong
learning. More knowledge is therefore needed to understand the of role of novelty seeking in
relation to both level and change in lifelong learning. Because novelty seeking seems to be an
important factor, it might be a great challenge for any society, company or organisation to under-
stand how individuals with low levels of novelty seeking can be triggered to engage in learning
activities when needed. Correspondingly, it may also be important to understand how to develop
strategies for both fostering and maintaining the feeling of novelty for individuals with high levels of
this personality trait, in order to maintain their curiosity and interest and reduce their feelings of
boredom.
Finally, in terms of novelty-seeking, it is important to note that the results of this study may be in
alignment with those identified by Offerhaus (2013) who found that individuals with higher levels
of openness, a personality factor derived from the five-factor model of personality, were more likely
to participate in employment-related further education and training. Openness, as previously
mentioned, is associated with curiosity and a tendency for engaging in new experiences (Costa &
McCrae, 1992), which to a certain degree conceptually overlaps with TCI’s novelty-seeking.
12 D. E. SÖRMAN ET AL.

Previous research has also found a significant correlation between openness and novelty-seeking
(Aluja & Blanch, 2011; Capanna et al., 2012; MacDonald & Holland, 2002). Furthermore, this
current study found a relationship between self-transcendence, defined by Cloninger et al. (1993) as
related to spirituality and an individual’s perception of themselves as part of the universe, and
overall engagement in lifelong learning. This factor has also been shown to significantly overlap
with openness (Aluja & Blanch, 2011; MacDonald & Holland, 2002), probably because openness as
well reflects an individual’s tendency to explore their inner life (Costa & McCrae, 1992), suggesting
a conceptual overlap between these factors. Therefore, the overall findings of this study are some-
what consistent with those reported by Offerhaus (2013).
However, it may still be somewhat challenging to interpret the relation between lifelong learning
and self-transcendence. Speculatively, however, it may be that such individuals are less anxious
when it comes to trying new learning activities. Research has shown that self-transcendence is
related to more positive emotions, optimism, and higher self-esteem, and to lower levels of
depression and neuroticism (Reischer et al., 2020). Such aspects may have a positive impact on
both the probability of engaging in new learning activities and the expectations of the outcome of
such engagement. Future studies will need to confirm whether self-transcendence is a personality
trait that may be related to lifelong learning. If so, investigating how and why this trait might
influence lifelong learning could be fruitful, and could potentially inform methods to promote
lifelong learning.
Finally, the latent growth curve analyses did not show a relation between harm avoidance and
overall engagement in learning activities. However, additional analyses revealed that this trait was
negatively related to the probability of changing occupation. Individuals with high levels of worry,
pessimism, and doubt thus seem to avoid such a change. Changing occupation may, depending on
the circumstances, be important both for individual development and for adaptation to societal
change, but it may also be more challenging on an individual level in comparison to the other
learning activities included in this study. The finding that harm avoidance may play a role in
occupational change is important knowledge; for instance, when it comes to supporting a career
change in cases where this may be necessary and/or fruitful. It is possible that individuals with high
levels of harm avoidance need more encouragement and support to give them the courage and/or
motivation to be able to change occupation.
None of the other personality factors in this study were related to level of engagement in
lifelong learning. However, it is still possible that these traits might be related to learning
activities other than those included in this study, and/or to engagement in activities not actively
initiated by the individual. As an example, individuals highly driven by reward or relief of
punishment, who react strongly to reinforcement and approval from others, and who are
dependent on social connection (i.e. reward-dependence), as well as individuals with high levels
of persistence in the presence of fatigue or frustration (i.e. persistence), may engage to a greater
extent in learning activities that are not primarily driven by intrinsic motivation and curiosity;
for instance, in situations when learning activities have been initiated by an employer and/or
when there is a social expectation of the individual. Similarly, it may be that individuals who are
responsible, dutiful, and self-accepting (i.e. self-directness) and/or who identify with and accept
others (i.e. cooperativeness) have other motives for engagement in learning activities. Future
studies should therefore investigate the impact of these personality traits on other features of
lifelong learning, and on engagement in activities not primarily undertaken due to the indivi-
dual’s own interest or motivation.
Overall, the results from this study show that novelty seeking is an important factor to consider
in relation to lifelong learning. One factor that may be important in the context of novelty seeking
and the curiosity to learn is that previous positive learning experiences influence learning self-
efficacy, motivation, and the likelihood of engaging in new learning activities (Sanders et al., 2015).
Conversely, previous negative experiences of learning can decrease an individual’s likelihood of
engaging in new learning activities (Illeris, 2006; Sanders et al., 2015. New experiences of teachers,
INTERNATIONAL JOURNAL OF LIFELONG EDUCATION 13

pedagogues, and working methods may therefore be important to (re-)create curiosity and desire
for learning, and perhaps even more so for individuals who have negative experiences of learning.
Considering also the predictive power of number of years in school for engagement in future
learning, as confirmed in this study, and the fact that positive learning experiences influence the
willingness to learn new things, it is likely that the school environment at young ages, where some of
the desire and curiosity to learn is developed, not only has a great impact on the number of years in
education, but may also increase the likelihood of engagement in lifelong learning.
Interpreting additional ndings of this study
Among the confounders included in the analyses, a greater number of years of education was
significantly and positively related to lifelong learning, and older individuals were found to be less
likely to engage in learning activities. Both these findings are in agreement with previous research
(see e.g. Macleod & Lambe, 2007; White, 2012). That individuals tend to be less inclined to engage
in new learning activities as they age is, of course, part of the challenge when it comes to motivating
individuals to lifelong learning. Age was however not related to slope (i.e. change). Thus, indivi-
duals who are already older and engaged in learning activities may not necessarily become less
engaged over time. Again, the great challenge seems to lie in creating a curiosity for learning early in
life, as this then seems to follow the individual through their lifetime.
The results also revealed that in comparison to men, women showed higher engagement in
taking courses over their working lives, and decreased probability of changing occupation. One
possible explanation for these findings could be that women are overrepresented in certain
occupations, such as the caring professions, which may give less opportunity to take courses.
However, the data available for this study did not allow us to confirm or disprove this. Similarly,
the data at hand do not show whether working in certain professions gives less opportunity to
change occupation.
Higher levels of occupational complexity were related to higher levels of attending courses; this
could be because occupations with high complexity are usually correlated with higher education, so
the individual may already be interested in competence development and learning new skills.
Finally, the results also showed that having more rooms in one’s household (i.e. indicative of higher
socioeconomic status) was related to less engagement in new educations. This result presents
a challenge in terms of interpretation. One potential explanation could be that individuals with
more rooms in their household have already attained a socioeconomic standing that reduces their
necessity for pursuing further education; this reasoning, however, is highly speculative.
Strengths and limitations
This study has several strengths. The participants were selected through stratified random
sampling, and it has previously been shown that the Betula sample has good population validity
based on aspects such as education, income, gender, marital status, and number of persons in
the household (Nilsson et al., 1997). Other strengths are the broad follow-up period and the use
of a relatively large sample size. Thus, the results from this study can be considered as robust
and reliable. However, some limitations should be acknowledged. One is the lack of information
about motives that may influence both engagement in learning activities and the relationship
between personality and lifelong learning. Previous research has shown that factors such as
better employment prospects, higher income, social benefits, and health issues may play a role
(see e.g. Maclean et al., 2013). Other factors may also cause individuals to make changes in their
lives; for instance, individuals may change occupation due to layoffs and/or closures.
Unfortunately, such information is not available in the Betula database in relation to learning
activities. Another limitation of this study is the number of learning activities included. We have
14 D. E. SÖRMAN ET AL.

used the information available in the database, but we are aware that this information is not
fully comprehensive.
Another factor that can limit and affect the opportunity to engage in learning activities is the
number of children in a household. The number of children is a potential confounder for
which, unfortunately, there is no specific information available in the Betula database. We used
‘number of people in household’ as a covariate in the analyses but are aware that this does not
fully capture this aspect. However, it should be noted that in Sweden the majority of households
do not practise generational living (Joelsson & Ekman Ladru, 2022), and it is often the children
who increase the number of people in the household. Future research should, however, consider
the family situation by using more precise measures regarding the number of children in the
household.
As previously noted, the total sample size significantly decreased over time. Even though the
sample characteristics remained relatively stable over time, and attrition could to some extent be
explained by the study design and exclusion criteria, there is always some risk of bias in the results
due to changes in the sample size over time. This should be carefully considered when interpreting
our findings.
Finally, the last data collection in this longitudinal study was executed between 2008 and 2010.
Since then, society has undergone substantial changes, which have brought both new professional
challenges and contributed to new forms of learning. For instance, the rapid digital transition that
occurred during the Covid pandemic still influences society and learning activities today. We cannot
rule out that the impact of personality on engagement in new learning activities may demonstrate
a pattern different from those found in this study. However, it is also possible that personality traits
such as novelty seeking and harm avoidance may have an even greater influence as society’s demands
for new forms of learning increase. For instance, even though it may not be immediately linked to
learning activity, it has been shown that novelty seeking does have a positive effect on the use of
modern technology, which has seen significant growth since this study had its last data collection, for
example in terms of the use of Mobile Instant Messaging (Cruz-Cárdenas et al., 2021). Future studies
should, however, further investigate the role of the personality traits included in this study in
engagement in both other and new forms of learning activities, by following individuals over time.
Conclusions and future directions
The present study demonstrates the importance of considering personality in relation to engage-
ment in lifelong learning. Novelty seeking seems to be a factor that plays a key role, but self-
transcendence and harm avoidance also appear to be influential. However, more studies are needed
to confirm these findings, and there is a need to increase the understanding of how and why such
traits may influence engagement in learning activities. Such knowledge could potentially increase
the likelihood of finding methods to promote lifelong learning.
In addition, there is a need to investigate the impact of personality on learning activities
other than those included in this study, and other personality dimensions should be
considered too. Future studies should also investigate personality in relation to the motives
and/or the cause for engagement in learning activities. Finally, none of the variables included
in this study were predictive of change in learning. Future studies should therefore aim to
identify factors that may predict change, in order to increase the understanding of what
makes individuals more (or less) likely to engage in lifelong learning over time.
INTERNATIONAL JOURNAL OF LIFELONG EDUCATION 15

Acknowledgement
The authors of this paper would like to express their gratitude for the collective wisdom and support of several
remarkable individuals outside academia, whose contributions have been invaluable. We extend our deepest
gratitude to Helena Sjöström of Nox Consulting, Katarina Pietrzak of RISE, Carina Wedin of the Transition Fund
(Omställningsfonden), and Jonah Tvelin of the Park. Each has generously provided general advice, offered new
perspectives, and facilitated connections with a wider community interested in lifelong learning
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
The work was supported by the VINNOVA [2021- 02361].
ORCID
Daniel Eriksson Sörman http://orcid.org/0000-0002-2709-9966
Elisabeth Åström http://orcid.org/0000-0003-2906-5409
Rolf Adolfsson http://orcid.org/0000-0001-9785-8473
Jessica Körning Ljungberg http://orcid.org/0000-0001-5546-3270
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