The relationship between students’ subject preferences and their information behavior

This document investigates the relationship between the preferred choice of school subject and student Information Behaviour (IB). 152 students, teachers, and librarians participated in interviews or focus groups. 1375 students, Key Stage 3 (11-13 years) to postgraduate, responded to a questionnaire. The research population was drawn from eight schools, two Further Education colleges, and three universities. Insights from the literature review and the qualitative research phase led to a hypothesis that was investigated using the questionnaire

This is a pre-publication version of the paper. An open source version of the published version is available at
https://www.emeraldinsight.com/doi/full/10.1108/JD-07-2017-0097
https://pan.baidu.com/s/18e4xbrHKNHGGEMiVAwpY0g
Title: The relationship between students’ subject
preferences and their information behaviour
Abstract
Purpose
This paper investigates the relationship between preferred choice of school subject and student Information
Behaviour (IB).
Design/methodology/approach
Mixed methods were employed. 152 students, teachers and librarians participated in interviews or focus
groups. 1375 students, Key Stage 3 (11-13 years) to postgraduate, responded to a questionnaire. The
research population was drawn from eight schools, two Further Education colleges and three universities.
Insights from the literature review and the qualitative research phase led to a hypothesis which was
investigated using the questionnaire: that students studying Hard subjects are less likely to engage in deep
IB than students studying Soft subjects.
Findings
Results support the hypothesis that preferences for subjects at school affect choice of university degree. The
hypothesis that a preference for Hard or Soft subjects affects IB is supported by results of an analysis in
which like or dislike of Maths/ICT is correlated with responses to the survey. Interviewees’ comments led to
the proposal that academic subjects can be classified according to whether a subject helps students to
acquire a “tool of the Mind” or to apply such a tool. A model suggesting how IB may differ depending on
whether intellectual tools are being acquired or applied is proposed.
Practical implications
The “inner logic” of certain subjects and their pedagogies appears closely linked to IB. This should be
considered when developing teaching programmes.
Originality/value
The findings offer a new perspective on subject classification and its association with IB, and a new model of
the association between IB and tool acquisition or application is proposed, incorporating the perspectives of
both teacher and student..
Keywords
Information behaviour, disciplines, critical thinking, students, information research, schools, universities
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1. Introduction
This paper reports selected findings from a study funded by the Arts and Humanities Research Council
(AHRC) entitled “Developing Deep Critical Information Behaviour”. The project ran from 2010 - 2012 and
investigated the nature, extent and pattern of occurrence of deep and surface Information Behaviours (IBs)
amongst students at secondary school and university, studying in England. It also sought to investigate the
extent to which, in the view of key stakeholders (i.e. educators, librarians and administrators), the
development of deep IB could be enabled and fostered. The project used a mixed-methods approach: stage
1 comprised interviews and focus groups with learners and stakeholders; and stage 2 consisted of a
questionnaire-based survey carried out in schools, colleges and universities in South Yorkshire and the
Midlands.
The analysis reported here focuses on the relationship between students’ preferred choice of school subject
and their IB (as self-reported, or reported by stakeholders). As well as probing respondents’ attitudes
towards using information for their school work, the questionnaire survey asked them to nominate the
subjects they most liked and disliked on the English National Curriculum. Thus this paper connects two areas
of IB research: learners’ IB in formal education, and differences in IB relating to academic discipline.
The underlying research problem is that of young people’s critical engagement with information. Julien and
Barker (2009, p. 12) noted that:
“Widespread concern about a lack of searching skills and critical information evaluation skills,
particularly among students, is evident in the literature. … Taken as a whole, then, the research
literature on information literacy skills among students demonstrates that students' skills are
generally lacking.”
These concerns have not abated in the intervening years (Goldman et al., 2016). For example Coiro et al.
(2015) found that only 25% of a stratified random sample of North American 12-13 year olds used
appropriate criteria to judge information quality. Indeed, the information landscape has become more
complex and problematic: recent studies have indicated that people cede critical judgements to search
engines (Sundin et al., 2017) and lack critical awareness of news filtering (Powers, 2017).
Definition of terms
In the context of this study, information is held to be “a stimulus which … amends the World View of the
informed” (Madden, 2004, p. 9). In other words, when students see or hear something that alters their
understanding of the world and/or their place within it, they are considered to be receiving information.
This understanding of information accords well with prevailing views of IB. Wilson, who originally coined the
term Information Behaviour (Wilson, 1981), defined it as:
"the totality of human behavior in relation to sources and channels of information, including both
active and passive information seeking, and information use. Thus, it includes face-to-face
communication with others, as well as the passive reception of information as in, for example,
watching TV advertisements, without any intention to act on the information given.” (Wilson, 2000, p.
49).
As Case (2016, p. 6) notes, IB therefore includes browsing and encountering information; and the study of
IB addresses "how people need, seek, give and use information in different contexts” (Pettigrew, Fidel and
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Bruce, 2001, p. 44). These contexts may be everyday life, life out of the ordinary (e.g. transition or crisis), the
workplace, or formal education. This paper considers aspects of IB that relate to the latter and explores the
question of whether students’ subject preferences and choices impact on the extent to which they seek for,
evaluate and use information and the ways in which they go about it.
2. Literature review
This review focuses on three key areas:
(1) Research which provides frameworks for analysing students’ critical thinking, IB and learning;
(2) Research into learners’ epistemological understandings;
(3) Disciplinarity, and research into disciplinary differences in students’ IB and learning, and in teachers’
approaches to teaching.
The review finishes by describing key features of the context for the research study: i.e. the English
secondary and tertiary education systems.
2.1 Frameworks for students’ critical thinking, Information Behaviour and learning
This section identifies research-based frameworks which can be used to categorise students’ approaches to
critical thinking, IB and learning: in particular those of Ford (1986), Marton and Saljo (1976, 1984), Newble
and Entwistle (1986), and Entwistle et al (2004). This work informed development of the project aims and
was used to frame survey questions relating to students’ approaches to engagement with information for
school work.
In a study of learners’ ways of thinking about their learning, Ford (1986) interviewed 34 education students
and proposed a model which focused on learners’ engagement with differing viewpoints on a topic. His
findings suggest that students may engage in deep and/or surface levels of critical thinking and that different
IBs are associated with different levels of critical thinking.
The categories Ford identified were:
● Sub critical thinking: the learner is either unaware of alternative viewpoints (unconscious sub critical
thinking), or is aware of them but does not evaluate them (conscious sub critical thinking). Ford’s data
suggested a number of possible reasons, including a lack of intellectual development; the perception that
evaluation is not required for an assignment; lack of interest, or that the subject of study may be purely
factual.
● Surface critical thinking: the learner evaluates different viewpoints, but “clinically, without personal
involvement” (Ford, 1986: 55). Such learners use criteria which they judge to be most acceptable to those
marking an assignment. Possible reasons for taking this approach include time pressure, a focus on marks
and a lack of interest in the subject.
● Deep critical thinking: the learner evaluates varying viewpoints “to a personally satisfying level” using a
number of information sources. Learners engaged in such thinking focus on working out their own viewpoint
based on criteria that are satisfying to them. The criteria espoused by students may vary from the criteria
used by assessors: in such cases a learner may adhere to his or her own perspectives even if it results in
lower marks.
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The whole information search process is implicated in Ford's categorisation. Although his focus is on
evaluating alternative viewpoints, this is only possible if someone has recognised that they may exist; has
selected search sites which may reveal them, and has chosen candidate sources from the material found.
Thus an orientation to deep critical thinking will tend to be associated with relatively wide independent
information seeking. This is in contrast to the IB of students wishing to find what they perceive to be a
factually correct answer (sub critical) or students focusing on reproducing what they think their teachers
want them to say (surface critical). The IB of students in the latter categories is likely to be more dependent
on reading lists and/or other specific direction by teachers.
Ford related his research to earlier work into deep and surface approaches to learning. Marton and Saljo
(1976, 1984) investigated the approaches to learning of Swedish social science students by asking them to
read a text. The students were then interviewed about their understanding of the text and their approach to
the task. Using what to was become known as the phenomenographic approach, Marton and Saljo (1984)
categorised learners as having either a deep approach to learning (seeking to understand the meaning of the
text by engaging with it holistically) or a surface approach (atomistic, snatching at parts of the meaning and
not attempting to see the whole picture). Entwistle (1998) has linked a deep approach to learning with
intrinsic motivation of the learner (motivated by feelings of personal satisfaction).
2.2 Deep critical thinking and deep critical IB
Ford (1986) concluded that the types of thinking and their associated IBs which emerged from his study are
likely to be influenced by personal (learner) variables. These include learning style, personality,
epistemological levels, and strategic choices linked to extrinsic or intrinsic motivational orientations.
Although the description of Ford’s deep critical thinking category above, implies deep critical IB, in being
open to acquiring, selecting and engaging with information that contributes to different perspectives, the two
activities need not be synchronous. We elaborate this relationship in Table 1.
Deep critical thinking may take place in the absence of deep critical IB if information is not explicitly sought or
encountered in relation to that thinking. This may happen, for example, when a person engages in deep
reflection about the logic of different ideas or arguments, isolated from information sources, even though the
ideas may have been acquired by past IB.
Table 1. Characteristics of deep critical thinking and deep critical IB
INSERT TABLE 1 HERE
A person may think critically about an argument they have encountered, but may not critically evaluate the
information source that provided the argument. Conversely, a person may critically evaluate the reliability of
an information source and question its representation of a particular argument, whilst accepting the argument
in an uncritical way.
2.3 Other studies linking IB with approaches to learning and thinking
Limberg (1999) worked with 25 Swedish high school students who had been given the task of researching
the consequences of a possible Swedish EU membership. After interviewing the students about their
information seeking, she identified three qualitatively different categories of information seeking experience.
In category A (labelled ”Fact–finding” by Limberg), ease of access was regarded as important and few
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sources were used. This accords with Ford's (1986) subcritical approach. By contrast, students with
Category C information seeking experiences (”Scrutinizing and analysing”) engaged with a variety of sources
in order to understand a topic. Limberg (with reference to Marton and Saljo, 1976) related these categories to
a surface/deep learning approach to research: “Students made efforts to relate information sources to one
another, to interpret information with regard to underlying motives and values and to relate parts to whole in
order to discern patterns and construct their understanding of the topic” (Limberg, 1999: 126). This focus on
their own understanding indicates a deep critical approach to the information task.
Students with Limberg’s category B experiences (“Balancing information in order to choose right”, Limberg,
1999, p. 126), did not engage critically with the ideas. As a result, they found it difficult to identify materials
relevant to both sides of the argument and thus present the “balanced” view they thought necessary. Thus, in
Ford’s (1986) categorisation, they might be described as actually engaging in sub-critical thinking (not
evaluating arguments) but oriented to trying to engage with critical thinking (realising that some evaluation is
necessary, whilst failing to achieve this). Their inability to identify the bias and nuances in the material they
examined meant that they could not even achieve a surface critical approach.
Connaway et al. (2011), in a mixed methods, multi-phase investigation into IB and sense-making, found that
both student and faculty respondents identified convenience (both in terms of access and time) as a critical
factor in shaping their searches. One strand of the IB literature identifies the widespread practice amongst
students of this “least effort” approach to information seeking, evaluation and use, with relevance being
judged on the basis of convenience, rather than critical evaluation of the information available (Dresang,
2005; Heinström, 2006; O’Brien and Symons, 2005; Urquhart and Rowley, 2007).
There is evidence that such a “least effort” principle covers different stages of the information seeking
process: failing to identify information needs (Walraven et al., 2008); selecting only familiar search tools (with
reliance on major search engines: establishing what Olsen and Diekema (2012) describe as an “information
comfort zone”); adopting unsophisticated search strategies (Timmers and Glas, 2010); failing to reduce large
volumes of information; spending little time assessing the accuracy, authority or relevance of sources before
drawing on them for academic work; and accepting poor quality information (e.g. Georgas, 2014; University
College London, 2008; Julien and Barker, 2009; Branch, 2003).
In many studies there is implicit or explicit criticism of the sources that learners turn to as convenient and
familiar, notably when these sources are Google and Wikipedia. For example, Judd and Kennedy (2011), in
their study of medical students' IB at a North American university, noted that first year students used Google
and Wikipedia for nearly 80% of their searches. They suggest that
“students' reliance on and familiarity with known tools such as Google and Wikipedia may be contributing to
underlying information literacy problems” (Judd and Kennedy, 2011, p. 359).
Julien and Barker however, report an “uneasy tension” (Julien and Barker,2009, p3) amongst high school
students using Wikipedia, suggesting that IB associated with its use is more nuanced than some
commentators imply, In particular, activities in which students engage in critiquing and editing Wikipedia
have been identified as improving learners’ critical abilities (e.g. Walker and Li, 2016).
So far the focus has been on learners' characteristics: however, in a formal education context, learners are
normally seeking information for a specific academic task, in a given learning and teaching context. Newble
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and Entwistle (1986) added a third approach to learning, in addition to "surface" and "deep": the strategic
approach. This is
“much more influenced by the context than by the nature of the task itself. In any given learning
situation there may be some difficulty in distinguishing them from students using deep and surface
approaches because they use whatever process they believe is likely to achieve high grades.”
(Newble and Entwistle, 1986, p. 168)
Newble and Entwistle (1986) note that the context for learning (notably, how the teachers teach) will have an
impact on the learners’ approach to learning. Both Newble and Entwistle’s (1986) strategic approach to
learning and Ford’s (1986) surface critical approach can be identified as extrinsically motivated strategies
(e.g. motivated by external rewards). This brings into the picture the learning task, the teacher setting the
task, and the educational context in which it is set. Influential on all these elements is the nature of the
discipline or subject to which the task relates.
This relationship was elaborated in the research-based model that emerged from the large scale project
investigating Teaching-Learning Environments in Undergraduate Courses (Entwistle et al., 2004). This
identifies the teacher, and his/her subject knowledge and pedagogical beliefs, and the influence of the
(disciplinary) academic community, as well as learners’ characteristics, experience and approach to study,
as important determinants of the quality of learning (Entwistle et al., 2004). These elements will be
elaborated in the following sections.
2.4 Learners’ epistemological understandings
The epistemological beliefs of students (i.e. beliefs about learning and knowing) also appear to affect their
Information Behaviour. Schommer (1990) proposed five dimensions of personal epistemology: “(a)
"Knowledge is simple rather than complex" (Simple Knowledge), (b) "Knowledge is handed down by
authority rather than derived from reason" (Omniscient Authority), (c) "Knowledge is certain rather than
tentative" (Certain Knowledge), (d) "The ability to learn is innate rather than acquired" (Innate Ability), and (e)
"Learning is quick or not at all" (Quick Learning).” (Schommer, 1990: 499).
Schommer (1990) tested her ideas by administering a questionnaire with items relating to each dimension,
and following this up with comprehension experiments. She found that the learners’ epistemological beliefs
seemed to affect how they interpreted the information they found. Thus for example, when students with
“strong beliefs in the certainty of knowledge” (who would map well onto Ford’s (1986) sub-critical or surface
categories) came across more ambiguous content, their view of knowledge lead “to the distortion of
information in order to be consistent with this belief” (Schommer, 1990: 503).
Such distortions may have an impact on the way that students search for information. A sample of 290
Iranian undergraduate students who completed an Epistemic Beliefs Inventory (based on Schommer’s work),
together with a questionnaire on IB based on the model developed by Wilson (2000) showed a positive
correlation between the sophistication of their epistemic beliefs and their searching behaviour: i.e. those with
more complex epistemic beliefs also had more complex approaches to IB (Mokhtari, 2014).
2.5 Critiquing the theoretical framework adopted for this study
The deep/surface distinction used as the theoretical framework for this study has arguably become widely
accepted and well established. It has been the subject of numerous studies across different levels of
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education (e.g. Booth, Luckett, and Mladenovic, 1999; Dart et al., 2000) and has been tested for validity and
reliability (e.g. Wong, Lin and Watkins, 1996).
However, the deep/surface dichotomy – particularly its normative associations whereby deep is deemed
preferable to surface – has been criticised as too simplistic, and as emanating and being sustained from a
limited Western perspective. Biggs (1994) noted the paradox whereby Chinese students emanating from
learning environments emphasising rote learning and memorisation in fact can display high levels of
achievement. In this context memorisation may be an integral part of coming to understand at a deep level.
Other authors have suggested that it may be a misconception to assume a dichotomous distinction between
deep and surface elements of learning, particularly in the case of high achieving Asian students (e.g.
Kember, 2000; Hung, Cheng and Wan, 2016; Webb, 1997).
Marton and Booth (1997, p. 39), however, drew on a study of teacher educators to identify that the Chinese
learner’s experience of memorisation may differ from that of a Western student
“For them, the process of repetition contributes to understanding because different aspects of the
text are in focus with each repetition, which is different from the mechanical memorization which
characterises rote learning.”
Webb (1997, p. 207), in his critique of phenomenography, goes further in suggesting that from a hermeneutic
perspective, a dichotomous distinction between deep and surface may not map onto the real world
complexity of learning:
“Learning, in this view, may be interpreted as the constant movement from part to whole. A simple
example for illustration may be seen in the relationship between understanding the words of a
sentence and the sentence itself. The idea of the hermeneutical circle describes movement from the
word to the sentence, back and forth, in a constant search of understanding and greater illumination.
It could just as easily be applied to the movement from surface (signifier) to deep (signified), back
and forth, in the quest for understanding. Such movement would, of course, be equally possible for
the Western and Chinese learner.”
However, it can be noted that a deep learning approach does not preclude paying attention to detail, and
may well involve examination of part and whole in a similar iterative way. It is also feasible that deep critical
and surface critical information behaviour could coexist and be intertwined; deployed by an individual in such
a hermeneutic circle.
It should also be noted that the well researched and established status, and relatively well developed
measures of the deep and surface learning constructs are not shared by the deep critical and surface critical
constructs which form the basis of the current paper. The former relate to understanding information, the
latter to seeking and critically evaluating alternative pieces of information. Information deeply understood
may not necessarily be subjected to the critical evaluation of its accuracy and value. Compared to deep and
surface understanding, deep and surface critical information behaviour have been the focus of less research
effort.. In this context, the current paper reports what is essentially an exploratory rather than definitive study,
offering tentative – though evidence-based – ideas to be further tested and refined in future research. Future
research could usefully investigate further alternative conceptualisations and measurements of deep and
surface critical information behaviour.
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2.6 Disciplinarity in relation to IB and teaching
Two ways of categorising disciplines have been particularly influential: those of Biglan, in particular as
adopted by Becher and Trowler (2001) and those of Whitley (2000). We chose to use the Becher/Biglan
approach to categorisation of disciplines for this study’s analysis as Whitley’s approach is more centrally
focused on the discipline’s research community, and less focused on the discipline’s epistemology.
In Biglan’s 1973 study, faculty members at the University of Illinois and at a small liberal arts college were
asked to make judgements about the similarity of academic areas. Their judgements were subjected to a
multivariate analysis from which Biglan identified the now familiar dimensions of Hard / Soft and Pure /
Applied. According to this classification, a Hard Pure discipline (e.g. physics) has strong predictability and
consensus about the most important research questions, and new knowledge emerges through discovery;
whilst a Hard Applied discipline (e.g. engineering) draws on hard pure knowledge but applies it in a practical
context so that the importance of research questions may be driven by real-world needs, and new knowledge
may be experienced as outcomes or products deemed useful.
By contrast a Soft Pure discipline (e.g. history) is likely to develop more recursively, with the field of
knowledge consisting of clusters of ideas rather than evident ‘building blocks’, and with a lack of consensus
on what constitutes an authentic contribution to knowledge. A Soft Applied discipline (e.g. education) draws
on other knowledge domains to interpret and understand situations, and the outcomes of research may be
protocols or procedures which aim to enhance social or personal life in some way (Webber, 2003: Becher
and Trowler, 2001).
Neither Becher and Trowler (2001) nor Whitley (2000) saw these as straightforward categorisations. All
considered the context of research (for example, the structure and politics of the higher education system) to
be crucial, and the problems of categorising a discipline (which may consist of varied and changing sub-
disciplines) were recognised. In the volume which succeeds Becher and Trowler (2001), Trowler et al. (2014)
debate the nature of disciplines in the 21st century, from a social practice perspective. They note increasing
impact from cross-disciplinary factors (e.g. economic pressures and a managerialist Higher Education
environment) which may mute disciplinary difference. However, they assert that “seismic shifts are not all in
one direction” (Trowler, 2014 p.38) with movement towards disciplinary silos as well as away from them,
towards interdisciplinarity. Trowler et al. (2014) conclude that there are still important differences in the way
that teaching and research is carried out in different disciplines,
Hjørland’s socio-cultural perspective has relevance here: he claims that
“tools, concepts, meaning, information structures, information needs, and relevance criteria are
shaped in discourse communities, for example, in scientific disciplines, which are parts of society’s
division of labor” (Hjørland, 2002, 258).
Whilst disciplinary discourse communities open to learners as well as teachers have tended to exist at
tertiary education levels, there is increasing interest, particularly in North America, in “Disciplinary Literacy”
for students at pre-University level. This movement fosters “reading for understanding”, and recognises the
multi-modal way in which young people read information, with “a focus on the specific ways a content area
thinks, uses language, and shares information” (Mongillo, 2017, p.331), even in the elementary classroom.
Goldman et al. (2016) outline a theoretical framework of learning goals for disciplinary literacy in history,
science and literature, and envisage formation of “classroom disciplinary communities” (Goldman et al., p.
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223). They propose “inquiry practices” and “forms of information representation” as two of the core
constructs which vary between disciplines. They contrast, for example, science practice, which builds
scientific knowledge through logical explanation and evidence, and historical inquiry, which weighs
competing narratives about past events, with the potential for unreliability and misrepresentation.
There is less research into disciplinary differences at the secondary education level than the tertiary, but
Madden et al. (2007) report on an English study into the behaviour of children seeking information for school
homework. Data collection methods included focus groups, and use of handheld devices to collect
information from a pool of 295 learners aged between 11 and 16. Students were asked about information
sources they had used for homework. The researchers found significant variation between the number of
resources used for the different subjects. The variation was particularly strong amongst Key Stage 4 (KS4)
students (aged 14-16) “where the mean number of resources ranges from 1.75 per homework (for Maths) up
to 3.00 (for Art)” (Madden et al., 2007, p. 350).
At the university level, Urquhart and Rowley (2007), in a model of IB derived from analysis of the large scale
Joint Information Systems Committee User Behaviour Monitoring and Evaluation project, identify “discipline
and curriculum” as a factor with direct impact on students’ IB. They reported discipline-related differences in
the use of e-journals as a specific example.
Whitmire (2002), in a survey of 5175 North American students found that students studying Soft subjects
(humanities, business, social sciences, and education) engaged in significantly more information seeking
activity than students studying Hard (physical sciences and engineering) subjects; and that students of Pure
subjects (physical sciences, humanities, and social sciences) engaged in more information-seeking activities
than those studying Applied subjects (engineering, business, and education). She also found that students
studying life subjects (social sciences and education) engaged in more information seeking than those in
non-life subjects (physical sciences, engineering, humanities, and business). Disciplines classified as social
sciences included “anthropology, economics, political science, psychology, sociology, etc.”
Students from Soft subjects also display more confidence in their information skills than those from Hard
subjects. Pinto and Sales (2014) received completed information competencies self-assessment
questionnaires from 1420 undergraduates from 17 departments in a Spanish university. Students from the
Arts, and the Social Sciences & Law faculties rated their skills higher than did students from the sciences,
health sciences and engineering faculties.
Whilst there are also some studies (e.g. Lee, 2008) which link differences in IB more to institutional and
personal contexts, there is a substantial body of evidence that links disciplinary differences to differences in
IB. Research evidence thus supports the view that disciplinary differences affect the way in which people
engage with information.
Urquhart and Rowley (2007, p. 1192) note however, that “it is important to recognize that these [differences]
are often closely linked with the type of learning and teaching strategies found in the discipline.” It is an
observation supported by Entwistle (2005) who, summarising results from case studies of ways of teaching
and learning in four disciplines, notes that there are differences between the way in which subjects are
taught, following the “inner logic of the subject and its pedagogy” [no pagination]. Neuman (2001) reviewed
research on disciplinary differences in approaches to university teaching. Findings included that Hard
disciplines "emphasise cognitive goals such as learning facts, principles and concepts" with "strong
9

emphasis on ability to apply methods and principles" whereas "Soft areas place greater importance on broad
general knowledge, on student character development and on effective thinking skills such as critical
thinking." (Neuman, 2001, p. 138).
Evidence for the impact of this approach in a Hard disciplinary context emerged, for example, in Svedin et
al.’s (2013) study of first year engineering students. They discovered that a surface approach was the
highest predictor of academic success, particularly for students of courses with a mathematics component.
They suggest that course design (e.g. focus on one examination) could discourage other approaches to
study.
Clearly, as these and other researchers have noted (e.g. Biggs, 1999: 30), and as was highlighted in section
2.3, a student’s approach to learning may be a response to the teacher’s approach to teaching, and thus is
not fixed. This might be most obvious where students tailor their response to what they believe are their
teachers’ requirements (a surface approach according to Ford, 1986); but it could also apply to deep critical
learners who have to decide whether to retain or abandon their own critically evaluated viewpoint in a
particular teaching context. Anderson and Johnston (2016) also note that a teacher with a dualist/realist
epistemology may in turn create a learning environment hospitable to an approach that:
“…would tend to discourage (either actively or by default) the kind of critical thinking that we argue is
an essential part of a full information literacy capability” (Anderson and Johnston, 2016, p. 59)
Thus, in examining learners' sub-critical, surface-critical or deep critical engagement with alternative
viewpoints, we can identify a number of factors that may influence the learners' behaviour. Most research
attention has been focused on learners: what motivates them? what different approaches to learning do they
have? how do they perceive knowledge? what is their relationship with particular (familiar) information
sources? Where IB is observed to be superficial, blame tends to be assigned (implicitly or explicitly) to the
learner for taking a "least effort" approach to the search.
However, we have identified other elements that are also implicated, namely: the task the student has been
set, the teacher setting the task, the overall learning and teaching environment, and (affecting all of these
elements) the discipline in which the task, teacher and learner are situated. Learner, task, and teacher
interact within the discourse and practices of the discipline, bounded by the learning environment.
2.7 The educational setting
In this section we describe key features of the English secondary and tertiary education system relevant to
our data collection approach and to interpretation of our findings.
In the methodology outlined below, two hypotheses are postulated; i.e., that preferences for particular school
subjects
1) have an impact (in the case of university students) on choice of degree and
2) are associated with differences in IB.
These are discussed in more detail in the methodology section.
Subjects studied in English schools are dictated by the National Curriculum. This was introduced as part of
the UK Parliament’s 1988 Education Reform Act. The Act identified four Key Stages (KS) of education
between the ages of 5 and 16, and specified attainment targets to be achieved by the end of each Key
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Stage. The National Curriculum was amended in the Education Act of 2002, and at the time when data was
gathered for this research study, all students studying at state schools in England, Wales and Northern
Ireland had, during KS 3 (11-14 years), to study the subjects listed in Table 1 (DfES/QCA, 2004).
Table 2: Subjects compulsory at KS3 (DfES/QCA, 2004)
INSERT TABLE 2 HERE
After completing KS3 studies, students in England have some choice in what they study. The most common
leaving qualification at age 18 is the Advanced Level (A level). Most English universities expect students to
join a named course and specialise immediately, so school children wishing to go to an English university will
select subjects that enable them to choose a degree that suits their interests.
There has been research into what affects students’ choices when they move from the core subjects taught
at KS3. Jin et al. (2010), in their report on subject and course choice by English school children completing
KS3 and KS4, identify that “like doing the subject” was the most frequent factor affecting decisions,
outstripping the next most frequent (“Knew I would do well in exam”) by nearly 30%. An earlier study into this
population (Blenkinsop et al.,2006) found that learners’ decision making processes were complex. The
authors typology of mindsets varied from “Confident aspirational” to “Defeated coper”; but it was noted that
learners’ decisions were likely to be based on what they currently enjoyed, even when their orientation was
towards the future.
However, in Maringe’s (2006) study of English students’ decisions about university courses, interest in
subject was only the fourth strongest factor, behind academic performance in a subject, career possibilities,
and teachers’ advice.
3. Methodology
The methodology adopted for the study consisted of two stages. First, a series of structured interviews was
conducted as outlined below (Table 3). Questions focused on students’ self-reported IB and on teachers’
observations of IB. Since students above KS3 have increasing control over their choice of subjects,
connections between academic discipline became more apparent in interviews with older students.
In the second stage, a questionnaire was developed in order to test a number of hypotheses. The resultant
quantitative data were subjected to statistical procedures comprising clustering and correlation analyses. As
well as subject preferences, the questionnaire was designed to investigate students’ IB – in particular levels
of deep engagement in processing information for their studies.
The hypothesis most relevant to the subject of this paper is that:
preferences for certain school subjects are associated with differences in IB. The research reviewed above
suggests that students studying Hard subjects with low task uncertainty are less likely to engage in deep IB
than students studying Soft, task uncertain subjects.
However, the data we collected relate to preferences while at school. Since many of the responses to the
questionnaire were from university students, we thought it prudent to test a second hypothesis, which is that
preferences for particular compulsory school subjects (KS3 National Curriculum) have an impact (in the case
of university students) on choice of degree. If, for example, a student expressed a preference for soft, pure
11

subjects at school, is that student more likely to study a soft, pure subject at university than a student with
different preferences?
3.1 Data collection
Interviews
In the first half of 2010, schools, Further Education (FE) colleges and universities in and around [city
Sheffield] were approached with details of the project and with a request for participants. This was to allow
study of a cross section of students at all educational levels from KS3 to postgraduate.
Co-operation was received from eight schools, two FE colleges and staff from three universities. All the
university students interviewed were from the University of Sheffield. They responded to a posting on the
University’s volunteer list, or were recruited via departmental contacts.
Data collection was carried out in accordance with the University of Sheffield’s policies on research ethics.
All interviewees were informed of the purpose of the project, were promised anonymity, and were assured
that they could withdraw from the project at any time.
Between November 2010 and July 2011, 152 students, educators, librarians and support staff from
participating establishments were interviewed. A summary of interviewees is presented in Table 3.
Table 3: Position of interviewees
INSERT TABLE 3 HERE
Questionnaire
The questionnaire (Appendix 1) was distributed to schools, colleges and universities in the north of England,
after being piloted in the University of Sheffield. In addition to the establishments referred to earlier, a further
three universities allowed the questionnaire to be distributed. Minor modifications were made according to
the nature of the educational establishment.
A total of 1375 students at educational levels ranging from Key Stage 3 (KS3) (11-13 years old) to
postgraduates (Table 4) were surveyed. All university students were recruited through volunteer lists and
completed the survey online. School students and students at FE colleges were given questionnaires by
teachers who had participated in the project. Students who chose to answer the questions handed the
completed forms to their teachers.
In order to ensure that respondents had had comparable school experiences, students in post-compulsory
education were asked if they were educated in England. Responses from students educated elsewhere were
excluded from the analysis.
Table 4: Numbers of UK-educated students responding to the questionnaire
INSERT TABLE 4 HERE
The questionnaire was designed to explore the role of the following factors in IB:
● Intrinsic/extrinsic motivation
● Engagement in deep critical IB
12

● Subject preference
● Level of educational experience
● Wikipedia use.
In addition, it asked students to nominate the three National Curriculum subjects they liked the most and the
three they disliked the most (see Table 2 for the full list of subjects). Students in post-compulsory education
were asked to select the subjects they liked and disliked “when you were at school”. Physical Education and
Music were excluded from the options because, up to KS3, the emphasis for these two subjects is more on
performance than traditional academic work (DfES/QCA, 2004).
3.2 Data analysis
The interview recordings were transcribed and analysed using a thematic approach. This iterative process
involved: inductive coding of emergent themes; coding of concepts in the theoretical framework (e.g.
quotations that could be interpreted as evidence of a surface approach to learning); coding using
frameworks, the relevance of which emerged during the analysis process, One such theme which emerged
early on was the association between subject of study and IB. Identification of this theme led to use of the
Biglan/Becher framework for disciplinarity, and the questionnaire survey being adapted to include questions
about subject preference. Several of the interviewees explicitly compared the information behaviour
associated with their subject to the IB they saw in other subjects. Quotations indicative of such comparisons
are presented in the Results section (4.2) below.
A cluster analysis was carried out on the lists of subject preferences declared by students in the survey, and
was used to generate the dendrogram in Figure 1. Dendrograms are tree-like diagrams “which indicate the
affinity of taxa to their nearest relatives” (Sokal and Rohlf, 1962). In this case the taxa were preferences for
subjects on the National Curriculum. The most commonly grouped preferences are indicated by the proximity
of subjects in the dendrogram (Figure 1); for example, students who prefer maths are also likely to prefer
science. The most fundamental separation of preferences (the right-most fork of the dendrogram) suggests
that students show a clear preference for either pure or applied subjects.
Figure 1: Dendrogram using Average Linkage (Between Groups).
INSERT FIGURE 1 HERE
Whitmire’s findings (Whitmire, 2002) clearly indicated differences between the IB of students studying Hard
and Soft subjects at US universities. This analysis was supported in part by Rozaklis (2012), but her findings
also suggested that a confounding factor may be whether a subject was pure or applied.
To test the effects of a preference for Hardness on IB, a variable was calculated based on students’
preferences for Maths and ICT (Tables 5a and 5b). Both can reasonably be regarded as Hard disciplines.
Since one (Maths) is Pure while the other (ICT) is Applied, a liking for both or a dislike of both suggests that
their Hardness plays a more significant part in the students’ stated preferences than whether they are Pure
or Applied.
Table 5a: Composite index based on student preferences for Maths and ICT
INSERT TABLE 5a HERE
13

Table 5b: Number of students who liked just one of the two subjects (Maths or ICT)
INSERT TABLE 5b HERE
Two subsets of the questionnaire data were therefore selected. Firstly, to test hypothesis 1, responses from
university students were analysed. Their subjects of study were given a score of 1 (Hard) or 2 (Soft). This
was correlated against their score for like or dislike of Maths & ICT (Table 6).
To test hypothesis 2, responses from the 105 students who expressed a liking for both Maths and ICT were
compared with those from the 130 students who expressed a dislike of both subjects (Tables 7-10) using
Spearman’s rank correlation.
Spearman’s rank correlation was used in both cases because it is a non-parametric test. It therefore makes
few assumptions about the data sets being compared. This makes it less sensitive than other tests of
correlation, so if a result is shown to be statistically significant, it can be accepted with greater confidence.
Spearman’s was also used to test whether preferences for particular school subjects had an impact on
choice of degree (Table 6).
4. Results
4.1 Quantitative analysis
The quantitative analysis was designed to test the hypotheses that preferences for particular school subjects:
1. are associated with differences in IB, and
2. have an impact (in the case of university students) on choice of degree..
The results summarized in Table 6 support the second hypothesis. If university students correctly recalled
their preferences at KS3, then those with a preference for Maths and / or ICT were more likely to study Hard
subjects than Soft subjects at university. It is useful to establish this association because the research
referred to in the literature review links IB to subject of study, particularly at university. The fact that level of
preference for Maths / ICT is highly significantly correlated to subject choice supports the relevance of the
cited findings to this study.
Table 6: Spearman correlations between maths / ICT preference index and choice of Hard or Soft
course at university. (N=758)
INSERT TABLE 6 HERE
The hypothesis that a preference for Hard or Soft subjects affects IB is supported by the results of an
analysis in which like or dislike of Maths / ICT is correlated with responses to the survey questions, as
discussed below, with reference to tables 7 to 9. These analyses used responses from both school and
university students.
Subjects associated with relative certainty
As might be expected, students who expressed a liking for Maths / ICT) were significantly more likely to
prefer subjects with clear right and wrong answers, and were less favourably inclined towards subjects which
gave them the opportunity to express their own opinions (Table 7).
14

Table 7: Significant Spearman correlations between the Maths / ICT index and questions relating to
subject preference. (N=235).
INSERT TABLE 7 HERE
Independent research
The significant correlations in Table 8 support the findings summarized in the literature survey above, that
students studying Soft subjects engage in more information seeking activity than students studying Hard
subjects. Students who disliked Maths / ICT were significantly more likely to report that they were required to
find information for their work (Table 8a). They were also more likely to feel confident of finding the
information they needed (Table 8b); to put effort into finding trustworthy sources (Table 8d), and to derive
satisfaction from doing so (Table 8c).
Students preferring Maths / ICT are significantly more likely to hold the view that they can get good marks
without really understanding the information they use (Table 8f). This may explain why they seem less
motivated to seek for and to evaluate information than students who dislike Maths / ICT (Table 8 e,g,h).
Table 8: Significant Spearman correlations between the Maths / ICT index and questions relating to
independent research. (N=234).
INSERT TABLE 8 HERE
Wikipedia
Frequent, uncritical use of Wikipedia has been seen as indicative of surface IB (Judd and Kennedy, 2011,
Rozaklis, 2012). Analysis of the questionnaire results revealed several differences in attitudes to Wikipedia
between students who liked and disliked Maths / ICT (Table 9). Those who liked the subjects were
significantly more likely to regard Wikipedia as a reliable source, to use information from it in their course
work, and to feel able to distinguish between accurate and inaccurate information (Table 9 a,c,d). They were
also significantly less likely to be told not to use it by their teachers (Table 9 b).
This could reflect teachers incorporating Wikipedia into academic practice: the Wikiproject Mathematics
started in 2002 (Wikipedia, 2017), for example. Tomaszewski and MacDonald (2016), in their study of
citations to Wikipedia in academic articles, identified the disciplines with the highest number of citing articles
as being Computer Science and Engineering, with Mathematics also making “notable use”. Similarly
Tohidinasab and Jamali (2013) identified Computer Science as the highest citer of Wikipedia, with Wikipedia
being used for facts and definitions as well as general background information.
Table 9: Significant Spearman correlations between the maths / ICT index and questions relating to
use of Wikipedia. (N=231)
INSERT TABLE 9 HERE
15

4.2 Relevant insights from qualitative analysis
The differences between the IB associated with Hard and Soft subjects were highlighted in an interview with
a final year physics undergraduate, who contrasted his IB with that of his girlfriend:
“you could do all of my subjects without looking in any book and that’s completely in contrast to
[name of girlfriend] who does law, who has to look in books. [In physics] The lecturers’ notes are
more than enough to get a first…”
His girlfriend, he observed “…is amazed about the lack of work I have to do. Because she has to read the
book and she has to learn these facts... Whereas I can just learn one technique.”
It was an observation corroborated by his girlfriend who noted (in a separate interview) that
“With law there’s just so much to read you couldn’t read all… I think part of what they want you to do
actually is to realize what you should be reading and what you shouldn’t be reading and what you
should be skim reading” .
In other words, she was expected to be sufficiently information literate to scope and plan her information
gathering, and to evaluate the material with which she engaged. For her to do well, she had to engage
critically with her reading material and demonstrate the ability to apply legal principles when interpreting it.
The physicist was probably underplaying the amount of effort required by most students attempting to master
the techniques to which he was referring. Once he had learned them however, he could demonstrate
mastery of his subject far more succinctly and unequivocally than his girlfriend could hers.
A similar point was made by a college lecturer who taught Maths and IT. He expressed the view that:
“…at the end of the day, there’s that sense of drilling that’s expected as a skill… It’s basically that
you do it by hard work – you do it by doing the right things in the most accessible way, and you get
your maths qualifications by working….
An A. level physics student at the same college recognised this “sense of drilling”:
“…rearranging equations is something… you have to go home and sort of see how it… slots
together, so things like little… techniques… they may expect you to know… you have to just go
home and just try and work it out.”
ICT, like maths, can be often done without recourse to information resources outside the classroom. In a
focus group comprising three BTEC ICT students at college, one of the students noted that: “With IT you
mainly just learn about the facts”. This was in contrast to his experiences of learning history at school where
he was presented with “two sides of a story” without it being possible to say “which one was right and
wrong.” It was an observation remarked upon by another student in the focus group:
“IT… it’s kind of like science or maths: you’re either right or you’re wrong. When you get an equation
there’s no two sides to the story. It’s just an answer and there’s no other answer.”
A comparison between maths and history was also made by one of the maths teachers interviewed. She
remembered her own early experiences as an undergraduate:
“…when I arrived at university I didn't understand there was such a thing as books about maths. I
thought maths was something you entirely did in your head, by yourself and solved all the problems
from first principles.”
16

Similarly, she expected her current, pre-university students
“to be able to solve problems independently by themselves, and to approach a different type of
problem and to come up with a good idea as to how to solve it. And they can only solve what they've
been very specifically taught to solve. That's probably the biggest problem.”
Reflecting on her own IB, she recalled:
“In terms of information gathering I think I learned most from history. I had a very good history
teacher when I was about fourteenish, who taught us very well about different sources of
information, and usually when I'm looking at the information I think of his opinions more than anyone
else.”
5. Discussion
The research considered in the literature review above supports the view that IB varies according to subject
of study. The evidence presented in this paper provides further support for this view as well as suggesting an
explanation (see below).
5.1 An Information Behaviour perspective of subject classifications
The quantitative analysis clearly indicates that students who expressed a preference for both Maths and ICT
were less likely to show deep critical IB than those who disliked the two subjects.
Whether that link is associative or causative is not clear, so two questions are raised that require further
investigation:
1. Are students with a preference for Maths / ICT and other Hard subjects less inclined to show deep
critical IB
2. Does their choice of subjects present them with fewer opportunities to do so?
The qualitative evidence presented in section 4.2 suggests that the latter is the case; and given the link
between subject preference at school and subject choice at university (Table 6), the former clearly remains a
possibility.
One of the points made by the students and teachers of Maths, ICT and physics interviewed was that their
subjects rely on drilling and the acquisition of techniques. Once acquired, these techniques allow students to
solve problems independently. Such problem-solving is typical of Hard subjects. Biglan (1973) suggested
that one of the features that determines whether disciplines are Hard or Soft is the presence of Kuhnian
paradigms. Kuhn may have agreed with this analysis. In describing “The Route to Normal Science” (Kuhn,
1996: Ch2), he argued that
“In the absence of a paradigm or some candidate for paradigm, all of the facts that could possibly
pertain to the development of a given science are likely to seem equally relevant” (Kuhn, 1996:15)
Where a discipline is in this state, Kuhn refers to it as pre-paradigmatic and suggests that the comparison of
conflicting ideas is necessary practice, but becomes less so with the emergence of paradigms. Thus, in hard
disciplines, which are more likely to have developed paradigms, it is less likely that students (particularly at
school level) will be called on to compare conflicting ideas fundamental to the discipline. Therefore there is
less opportunity for them to practice and develop deep critical IB.
5.2 Tool acquisition and tool application subjects
17

Paradigms, according to Kuhn are “intellectual tools”, which are
“…prior to, more binding, and more complete than any set of rules for research that could be
unequivocally abstracted from them.”
Applications associated with them
“…accompany the theory into the textbooks from which the future practitioner learns his trade” Kuhn
(1996, p. 46).
The idea of intellectual tools is well established in literature relating to the development of consciousness
(eg, Dennett, 1996). Richard Gregory, in considering factors that led to the development of science,
discussed various “tools of the Mind” (Gregory, 1981: 48), including speech, literacy and calculation. Later,
he compares what he terms “Potential Intelligence” and “Kinetic Intelligence”.
“The distinction here is between solutions (or part-solutions) already available, and solutions that
have to be discovered.”
Potential intelligence is associated with the former. Someone who is aware of the solution to an existing
problem can apply that solution and their intelligence to problems that would have been intractable (or
inconceivable) before the solution was arrived at:
“So education increases our ‘internal’ Potential Intelligence, through giving knowledge of what
problem-solvers or aids are available” (Gregory, 1981: 311).
One key way in which education achieves this is by providing learners with appropriate “tools of the Mind”.
At an elementary level, these include speech, literacy and calculation. Once acquired, these tools can be
used to gain competence in other tools (such as scientific paradigms).
The concept of intellectual tools provides a possible explanation for the observation that students of different
subjects display different IBs. In university subjects traditionally classified as Hard, students are seeking to
acquire the mathematical and paradigmatic tools needed to learn their “trade”, and are considered
successful if they can demonstrate an understanding of those tools and can independently solve problems
associated with them. So, the final year physics undergraduate felt able to complete his studies “without
looking in any book” and the college maths teacher, as an undergraduate “thought maths was something you
entirely did in your head, by yourself”. In both cases, the main focus of their efforts was the acquisition of
some challenging tools of the Mind. University students of Soft disciplines by contrast, are more likely to be
expected to demonstrate the application of intellectual tools acquired at school in order to evaluate, compare
and synthesize arguments. Hence, they will be required to engage critically with information from a range of
sources.
Unlike the categories Hard and Soft, classifications of tool acquisition or tool application are not fixed. A
subject may be a tool acquisition subject at one level of study and become a tool application subject at a
more advanced level. Scientific paradigms and mathematics are particularly significant sets of intellectual
tools, because of their technical difficulty and their relative cultural neutrality. All academic disciplines though,
require the mastery of basic techniques or the grasp of key concepts. Students of a foreign language for
example, will begin by acquiring basic tools of grammar and lexis. When they have achieved some degree of
18

proficiency, they will be able to apply those tools to an analysis of culture associated with the language of
study.
In English schools and colleges therefore, students studying for a General Certificate of Secondary
Education (GCSE) are required to demonstrate comprehension and an ability to communicate in the
language of study (Department for Education, 2013). Advanced Level students must, in addition, “engage
critically with intellectually stimulating texts, films and other materials in the original language, developing an
appreciation of sophisticated and creative uses of the language and understanding them within their cultural
and social context” and “develop knowledge about matters central to the society and culture, past and
present, of the country or countries where the language is spoken” (Department for Education, 2015).
Students of Hard subjects tend only to carry out such critical evaluations once they have demonstrated an
ability to use the intellectual tools associated with their discipline. This may not be until they have studied to a
postgraduate level. For example, a maths undergraduate, asked to solve a set of differential equations will
be able to do so without needing to refer to any other information source. If that same student goes on to
study econometrics, he or she may be required to compare and evaluate the assumptions that underpin
different mathematical models describing the same aspects of the economy. Shifts of this kind, from
acquiring to applying intellectual tools, would lead to more open and less fact-based questions. Such
questions would be more conducive to deep IB.
Some of the possible associations between IB and tool acquisition/ application suggested by the research
reported here are summarised in Figure 2. Students engaged in tool acquisition can often successfully rely
on a limited number of sources (e.g. textbooks, teachers, standard formulae). Such sources (whether
textbooks or Wikipedia) divulge well-established facts and explanations which are accepted as authoritative.
An example of such IB was discussed by Goldman et al (2016), who note the importance of the explanatory
argument in science, in contrast to the competing narratives explored by history students, who have to
consider the possibility that some of the narratives may not be true. Such a consideration is, however,
important to students who are determining how best to apply tools acquired through earlier study. As shown
in Figure 2, this is likely to affect associated information-related activity.
Whilst both tool acquisition and tool application subjects involve understanding as opposed to mere rote
learning, there is a different balance of emphasis between the two. An authoritative source in a tool
acquisition subject is one that accurately presents the core ideas: it is not necessarily the original source for
those ideas. With paradigmatic disciplines, this has the ironic consequence that reference to original
sources is often implicit, and they are used but not cited. So for example, a student of physics is far more
likely to look up Newton’s laws of motion on Wikipedia than to reference Naturalis Principia Mathematica.
However, Wikipedia’s description of the laws is probably easier for a 21st century student to memorise; and
memorisation has a prominent role in gaining mastery of tools of the mind .. This, arguably, results in a more
narrowly focused intellectual process than is found with tool application subjects, where the appropriateness
of different tools to particular contexts must be considered and evaluated. This links to Pask’s (1988)
observation that memory plays a more significant role in developing understanding of procedural aspects of
a new subject, whilst broader-based integration of new ideas into a learner’s existing knowledge is more
useful when developing a wider understanding of a subject in context.
19

Figure 2. A model of possible associations between IB and tool acquisition/application.
INSERT FIGURE 2 HERE
A learner’s epistemological development, learning approach, motivation, previous experience etc., all
influence IB, Figure 2, though, focuses on the disciplinary context and the role of the educator. The challenge
for the teacher in scaffolding transition from tool acquisition to tool application mode was well expressed by
one of the maths teachers interviewed in the course of this research, namely for students
“to be able to solve problems independently by themselves, and to approach a different type of
problem and to come up with a good idea as to how to solve it. And they can only solve what they've
been very specifically taught to solve. That's probably the biggest problem.”
The categories of IB considered in this paper derive directly from Ford’s (1986) classification of critical
thinking. Table 1 compares deep critical thinking and deep critical IB, and section 2.2 highlights some of the
ways in which the two behaviours may diverge. Such is the case in situations in which the identification and
evaluation of competing viewpoints or arguments are not required for the particular intellectual task being
performed. Tasks designed to demonstrate the successful acquisition of intellectual tools would be a case in
point.
In such instances, classification within Ford’s (1986) categories of critical thinking are inappropriate. A
category of “sub critical IB” would imply an evaluative judgement that the student has failed to engage in
appropriate information behaviour. The fact that critical IB is not relevant to the task may be more accurately
reflected by the term “a-critical IB”, which better represents a legitimate absence of critical information
behaviour.
.
6. Conclusions
This article has made a contribution by providing a new perspective on the relationship between subject
discipline and IB, by extending categorisation of IB with a new “a-critical” category”, and by proposing a
model of students’ engagement with information (Figure 2) using the novel lens of tool application and tool
acquisition.
A large body of research suggests a link between academic discipline and IB. This appears particularly
marked when comparing students of Hard disciplines with students of Soft disciplines. Students at schools,
FE colleges and universities in and around [city Sheffield], UK completed a survey of IB. Part of the survey
asked them to indicate their preferred subjects in the National Curriculum. Students with a liking for Maths
and ICT were taken as being representative of learners with a preference for Hard over Soft subjects. Their
responses to the questionnaire suggested that they were significantly less motivated to seek information, and
that there was less requirement for them to do so, than was the case with students who expressed a dislike
for Maths / ICT. Interviews with students and teachers suggested that, in subjects traditionally classified as
Hard, the main focus was on demonstrating the ability to understand and apply intellectual tools associated
with the discipline.
This focus on intellectual tools led to the suggestion that an academic subject can be classified according to
whether such tools are being acquired or applied. Such a classification offers more flexibility than Biglan’s
20

division of subjects into Hard and Soft because it recognises that a learners’ relationship with a subject may
change as the learner progresses. In the early stages of learning a discipline, the focus tends to be on tool
acquisition: students are expected to demonstrate a grasp of key terms and concepts. As they learn more,
they experience more opportunities to apply intellectual tools that were acquired earlier, both within that
discipline and in other areas of heir education. The change in relationship, from tool acquisition to tool
application, is reflected in changing information behaviour. Opportunities to apply previously acquired
intellectual tools come with more broadly focused assignments of the sort that promote deep, critical IB.
This suggested link between subject of study and IB supports observations by Urquhart and Rowley (2007)
and by Entwistle (2005). While it might be the case that some students take a “least effort” approach when
finding and evaluating information, it also appears that IB is sometimes influenced by the “inner logic of the
subject and its pedagogy” (Entwistle, 2005). The presence of such an “inner logic” suggests the need for an
important addition to the classifications of IB derived from Ford (1986) (Table 1). This should reflect
situations in which the identification and evaluation of competing viewpoints or arguments is not required, or
is not relevant to the particular intellectual task being performed. Such is likely to be the case where tools
are being acquired, and students are gaining practice in their use.
Extending the “sub critical thinking” category to include such circumstances would be inappropriate because
“sub” implies an evaluative judgement that the student has failed to engage in appropriate critical thinking, or
that such thinking is inherently less worthy than critical thinking. The fact that critical thinking is simply not
relevant to the task may be more accurately reflected by the term “a-critical thinking” which better represents
a legitimate absence of critical thinking.
It should be recalled however, that the quantitative data analysed in this study related to subject preference
rather than subject choice. There therefore remains the possibility that lack of desire to engage with
information may play a role in determining student preference. Further research in this area would be
valuable, and the findings of this article suggest possible directions for such research.
The model outlined in Figure 2 considers possible associations between IB and tool acquisition/application
and could provide a framework for investigating teachers’ perceptions of their subject by getting them to
place it within the model. The well-established Hard/Soft subject division has allowed some analysis of
association between IB and field of study to be carried out in a Higher Education context; but a classification
based on whether students are acquiring or applying “tools of the Mind” (Gregory, 1981: 48) suggests a
potentially valuable approach for analysing the information behaviour of students far earlier in their academic
careers. The inclusion of the teacher, the learner and the subject within the model (rather than just focusing
on the learner’s characteristics) provides a more holistic tool to examine practice within the classroom, and
also provides a framework for further research investigation.
Acknowledgements
This research was supported by a grant from the Arts and Humanities Research Council.
The authors would like to express their gratitude to the many staff at schools and colleges in and around [city
Sheffield] who helped to make this research possible, and to the reviewers for their constructive and helpful
criticism.
.
21

Ethical guidelines
The research was carried out in accordance with the University of Sheffield 's ethics policy (a link to the
policy will be given in the published version). All interviewees were provided with an information sheet
(available as a University of Sheffield ' Google drive document - a link to the document will be given in the
published version). This was presented to volunteers prior to the interview. An edited form of the sheet also
formed part of each questionnaire.
Conflicts of interest
No conflicts of interest were identified in the course of this research.
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26

Table 1. Deep critical thinking and deep critical information behaviour
Deep critical
thinking
The focus is
on the
viewpoint/
argument
Evaluate the
relative merits
of competing
viewpoints/argu
ments in
relation to a
particular issue
Viewpoint/argument (A) Viewpoint/argument (B)
Deep critical
information
behaviour
The focus is
on the
information
source
Evaluate the
relative merits
of different
pieces of
information in
terms of
contributing to
a varied and
nuanced
perspective on
the issue
Information
(A i) relating
to A
Information
(A ii) relating
to A
Information
(B i) relating
to B
Information
(B ii)
relating to B
Table 2: Subjects compulsory at KS3 (DfES/QCA, 2004)
Core Subjects Foundation Subjects
English Art & Design Modern Languages
Mathematics Design & Technology Music
Science Geography Personal, social, health and economic
education (PSHE)
History Physical Education (PE)
Information & Communication
Technology (ICT) Religious Studies
27

Table 3: Position of interviewees
Role of Interviewee Nature of establishment No.
Librarian School 5
FE 3
University 3
Admin & learning support School 3
FE 3
Educator School 5
FE 20
University 8
Student School (KS3) 33
School (KS4) 8
School (6th Form) 2
FE 31
University (UG1) 14
University (UG2) 5
University (UG3) 9
28

Table 4: Numbers of UK-educated students responding to the questionnaire
Key
Stage 3
Key Stage
4
A Level/
BTech
Undergraduate
(Years 1-3)
Undergraduate
(Years 4+)
Postgraduate
Total 203 121 255 559 53 165
M 104 55 110 199 21 49
F 99 66 145 360 32 116
Table 4a: Composite index based on student preferences for Maths and ICT
ICT Maths
(% likes)
1 Student likes both Maths & ICT 105 33.0 20.0
2 Student likes one 440 44.0 57.3
3 Student neither likes nor dislikes either 145 (% dislikes)
4 Student dislikes one 344 34.4 51.1
5 Student dislikes both 130 34.4 31.4
Table 4b: Number of students who liked just one of the two subjects (Maths or ICT)
Student likes Maths but dislikes ICT 119 5.4%
Student likes ICT but dislikes Maths 73 8.9%
Table 5: Spearman correlations between maths / ICT preference index and choice of Hard or Soft
course at university. (N=758)
Correlation Significance level
Hard / Soft
(Hard = 1, Soft = 2) 0.274 < 0.1%
Table 6: Spearman correlations between maths / ICT preference index and choice of Hard or Soft
course at university. (N=758)
Correlation Significance level
Hard / Soft
(Hard = 1, Soft = 2) 0.274 < 0.1%
Table 7: Significant Spearman correlations between the Maths / ICT index and questions relating to
subject preference. (N=235).
Correlation Significance
level
I like subjects that give me the opportunity to express my own
opinions
-0.327 < 0.1%
I like subjects where there are clear right and wrong answers 0.454 < 0.1%
(1=Strongly agree, 5 = Strongly disagree. Negative correlations indicate that students with a dislike of maths
/ ICT were more likely to agree with the statement than were students who preferred them.)
29

Table 8: Significant Spearman correlations between the maths / ICT index and questions relating to
independent research. (N=234).
Correlatio
n
Significance
level
a) When I am given a piece of work to do I have to find information for
myself in order to do the work
-0.328 < 0.1%
When I am given a piece of work that involves finding information for
myself…
b) I feel confident that I will be able to find the information I need -0.131 <5%
c) I get satisfaction from knowing my information has come from a
trustworthy source
-0.212 < 1%
d) I put a lot of effort into making sure I get my information from sources
that are trustworthy
-0.185 < 1%
When I am doing a piece of work that involves using information I have
found for myself
e) I get better marks when I focus on the most relevant information -0.208 < 1%
f) I can get good marks without really understanding the information I
am using
0.166 < 2.5%
g) I get satisfaction from knowing I have understood the information I
am using
-0.281 < 0.1%
h) I put a lot of effort into understanding how all the information fits
together before I use it
-0.151 < 2.5%
(1=Strongly agree, 5 = Strongly disagree)
30

Table 9: Significant Spearman correlations between the maths / ICT index and questions relating to
use of Wikipedia. (N=231)
a) Wikipedia is a reliable source 0.292 < 0.1%
b) My teachers tell me NOT to use Wikipedia for my school work -0.159 < 2.5%
c) I use information from Wikipedia in my school / university work 0.323 < 0.1%
d) I can tell whether the information on Wikipedia is true or not 0.134 < 5.0%
(1=Strongly agree, 5 = Strongly disagree)
Figure 1: Dendrogram using Average Linkage (Between Groups).
31

Figure 2. A model of possible associations between Information Behaviour and tool
acquisition/application.
32