learning hindrances and self-regulated learning strategies

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Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=cshe20 Download by: [UQ Library] Date: 31 May 2017, At: 16:33 Studies in Higher Education ISSN: 0307-5079 (Print) 1470-174X (Online) Journal homepage: http://www.tandfonline.com/loi/cshe20 Learning hindrances and self-regulated learning strategies reported by undergraduate students: identifying characteristics of resilient students Louise Ainscough, Ellen Stewart, Kay Colthorpe & Kirsten Zimbardi To cite this article: Louise Ainscough, Ellen Stewart, Kay Colthorpe & Kirsten Zimbardi (2017): Learning hindrances and self-regulated learning strategies reported by undergraduate students: identifying characteristics of resilient students, Studies in Higher Education, DOI: 10.1080/03075079.2017.1315085 To link to this article: http://dx.doi.org/10.1080/03075079.2017.1315085 Published online: 25 Apr 2017. Submit your article to this journal Article views: 111 View related articles View Crossmark data

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Page 1: Learning hindrances and self-regulated learning strategies

Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=cshe20

Download by: [UQ Library] Date: 31 May 2017, At: 16:33

Studies in Higher Education

ISSN: 0307-5079 (Print) 1470-174X (Online) Journal homepage: http://www.tandfonline.com/loi/cshe20

Learning hindrances and self-regulated learningstrategies reported by undergraduate students:identifying characteristics of resilient students

Louise Ainscough, Ellen Stewart, Kay Colthorpe & Kirsten Zimbardi

To cite this article: Louise Ainscough, Ellen Stewart, Kay Colthorpe & Kirsten Zimbardi(2017): Learning hindrances and self-regulated learning strategies reported by undergraduatestudents: identifying characteristics of resilient students, Studies in Higher Education, DOI:10.1080/03075079.2017.1315085

To link to this article: http://dx.doi.org/10.1080/03075079.2017.1315085

Published online: 25 Apr 2017.

Submit your article to this journal

Article views: 111

View related articles

View Crossmark data

Page 2: Learning hindrances and self-regulated learning strategies

Learning hindrances and self-regulated learning strategiesreported by undergraduate students: identifying characteristicsof resilient studentsLouise Ainscough, Ellen Stewart, Kay Colthorpe and Kirsten Zimbardi

School of Biomedical Sciences, The University of Queensland, Brisbane, St Lucia, QLD, Australia

ABSTRACTStudents in higher education face a variety of learning hindrances whilestudying at university. These hindrances may negatively impact onlearning by distracting from study, or may enhance learning byencouraging students to address challenges as they arise. In the currentstudy students were asked to describe their learning hindrances at asingle point early in semester, and to outline the strategies forovercoming these hindrances in future. Five hindrance clusters weredetermined and differences between student academic subgroupswere identified. Hindrances associated with difficulties understandingwere reported most frequently by improving students, who haddemonstrated resilience by passing second year biomedical sciencecourses after failing in first year. Improving students were also mostlikely to report learning strategies that promote understanding. Theseresults suggest that early interventions to encourage students tocritically evaluate their understanding may benefit struggling students.

KEYWORDSSelf-regulated learning;metacognition; engagement;high-risk students; learningstrategies

Introduction

The transition from secondary to tertiary education is particularly challenging for students as theynavigate a new learning environment (Kantanis 2000; van der Meer 2012; van der Meer, Jansen,and Torenbeek 2010). University students need to cope with changing academic expectations sur-rounding independent learning, time management and level of academic challenge. For many stu-dents these changes are compounded by a transition from adolescence into adulthood, whichcorrelates with increased social and financial independence (Kantanis 2000). For each student, thedecision to remain at university or not will depend on how well they adjust to university life andthe challenges or hindrances they face while at university.

Hindrances in life can be classified as either major life events, such as divorce or death of a familymember, or daily hassles, such as juggling too many daily tasks or sitting in a traffic jam (Kanner et al.1981). Daily hassles are the frustrations or stressors that we experience in everyday life, and althoughthey are less severe than major life events, an accumulation can impact on mental health and well-being (Chamberlain and Zika 1990). In the academic arena, hindrances classified as daily hasslesinclude academic deadlines, time management, balancing academic workloads or receiving lowergrades than anticipated (Blankstein, Flett, and Koledin 1991; Ross, Niebling, and Heckert 1999).Major life events specific to academia have not been investigated, although students may still experi-ence non-academic major life events during their time at university. In addition, students may

© 2017 Society for Research into Higher Education

CONTACT Louise Ainscough [email protected] School of Biomedical Sciences, The University of Queensland,Brisbane, St Lucia, QLD 4072, Australia

STUDIES IN HIGHER EDUCATION, 2017http://dx.doi.org/10.1080/03075079.2017.1315085

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experience chronic hindrances, such as repeated academic failure, or acute hindrances, like an iso-lated poor grade (Martin and Marsh 2008).

If these hindrances are severe or frequent they may result in disengagement with the learningprocess (Skinner and Pitzer 2012), which can lead to a lack of motivation or attrition from university(Krause 2005; Long, Ferrier, and Heagney 2006; Yorke and Longden 2008). Therefore, it is vital thatstudents at all stages of education are able to recognise and confidently respond to hindrances. Pre-vious studies on coping strategies have focused primarily on students who experience severe orchronic challenges to learning, such as low socioeconomic status, learning disabilities or belongingto a minority group. These studies have demonstrated that some learners are academically resilient,meaning that they perform at higher than expected levels when exposed to hindrances (Miller 2002;Morales 2008a; Perez et al. 2009). A variety of personal and environmental factors can support aca-demic resilience, such as confidence or self-efficacy (Borman and Overman 2004; Morales 2008a), aca-demic self-control (Borman and Overman 2004; Miller 2002), motivation or goal setting (Hutchinsonet al. 2004; Miller 2002), supportive family, peer and school environments (Hutchinson et al. 2004;Miller 2002; Perez et al. 2009) and participation in organised activities (Borman and Overman 2004;Hutchinson et al. 2004; Perez et al. 2009). Although it is clearly important to foster academic resiliencein high-risk students, all students are likely to experience the ‘minor’ hindrances that are considereddaily hassles, and student responses to such hindrances are less well understood.

The theoretical framework of self-regulated learning (Zimmerman 2000, 2002) provides an excel-lent basis for investigating the types of strategies students use to cope with hindrances and mayprovide some insight into how some are able to develop academic resilience. The self-regulatedlearning cycle describes the thoughts, feelings and behaviours that students use during learning,and includes the personal factors that have previously been associated with academic resilience,such as self-efficacy, motivational beliefs, goal setting and self-control (Borman and Overman2004; Hutchinson et al. 2004; Miller 2002). There are multiple models of self-regulated learning (Boe-kaerts 1997; Pintrich 2000; Winne 1996), which all provide a framework for learning that occurs cycli-cally within phases. Zimmerman’s model (Zimmerman 2000) has been applied to a variety of differentdisciplines (Aregu 2013; Bonner et al. 2002; Collins and Durand-Bush 2014), and consists of fore-thought, performance and self-reflection stages. Forethought occurs prior to a learning activityand involves analysing the task at hand by setting goals and planning for the learning event, aswell as motivational beliefs about the upcoming learning task, such as self-efficacy and interest.The performance phase includes learning strategies that students implement during learningtasks, such as reading textbooks or studying from notes, as well as monitoring the progress of learn-ing while implementing these strategies. The last phase is self-reflection, which includes efforts toevaluate one’s performance against standards, adapting learning strategies to enhance performancewith the next iteration, attributions of success or failure and one’s sense of satisfaction regarding thelearning task. Students vary in the degree to which they self-regulate, with some students demon-strating more well developed self-regulatory skills than others (Ning and Downing 2015; Yip 2007).

The current study aims to use qualitative analysis to expand on past research into the hindrancesexperienced by university students and to explore the types of self-regulated learning strategies thatstudents propose to use to overcome the hindrances they faced early in semester. In addition, thisstudy aims to determine whether there are any differences in either hindrances or learning strategiesbetween improving (resilient) and low-achieving students.

Methods

Participants and course structure

Participants were undergraduate pharmacy students at the University of Queensland, which is alarge, research-intensive university in Australia. Participants were enrolled in a second year physi-ology course for pharmacy students (BIOM2010) in either 2013 or 2015. In 2013, the cohort had a

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median age of 19 years, 18% were international students and 64% were female. In 2015, the medianage was 19% and 69% of the cohort was female; however, there was a higher proportion of inter-national students (41%).

BIOM2010 covered physiology and pharmacology of the endocrine, nervous, reproductive andgastrointestinal systems. Contact hours included three lectures per week. Assessment for thecourse consisted of a mid-semester exam (25%), five meta-learning tasks (15%) and an end of seme-ster exam (60%).

Learning hindrances and strategies

To identify learning hindrances, students were asked to:

Write down a major thing that has hindered your learning in BIOM2010 over the past few weeks. This could besomething specific to the course, but may also be something more general.

The following companion question was also asked at the same time:

Write down one strategy you could use in the future to reduce the impact of the above hindrance on your learn-ing. Regardless of whether you can control the source of the hindrance, what is one thing that you can controlthat may reduce the negative impacts of that hindrance on your overall learning.

Students were not given any guidance as to what might count as a major hindrance, so that studentresponses would be unbiased. These questions formed part of a series of ‘meta-learning’ assessmenttasks, which are described in Colthorpe et al. (2015). Each meta-learning task was completed indivi-dually online and consisted of six open-ended questions designed to prompt students to think abouttheir learning strategies and reflect on the learning process. The meta-learning questions relating tolearning hindrances and strategies outlined above were asked in either week 4 (2013) or 5 (2015),prior to the mid-semester exam. Students completed the meta-learning tasks at approximatelythree-week intervals during semester and were awarded marks for relevant and completeanswers. As these questions formed part of the course assessment, student responses were detailed,with 119 words ± 4 (mean ± standard error of the mean) for the 2 questions.

Student responses to the hindrance and strategy questions were analysed using thematic analysisin NVivo 10™ (QSR International, MA, USA). Learning hindrances were inductively analysed by iden-tifying common themes from the data (Braun and Clarke 2006). The accompanying strategies weredeductively analysed based on Zimmerman’s self-regulation cycle (Zimmerman 2000, 2002), and theself-regulatory strategies outlined in Nota, Soresi, and Zimmerman (2004), adapted for university stu-dents. Specifically, the Nota, Soresi, and Zimmerman (2004) category ‘organising and transforming’was changed to ‘transforming records’ based on the low occurrence of efforts to organise learningmaterials in our current cohort. The Nota, Soresi, and Zimmerman (2004) category ‘goal-settingand planning’ was separated into three categories: ‘goal-setting’, ‘planning’ and ‘time management’to capture the difference between well-articulated plans (planning) and general efforts to organiseone’s time better (time management). The strategies ‘attending lectures’ and ‘health-relatedchanges’ were also added. The Nota categories ‘self-evaluation’ and ‘rehearsing and memorising’were included in the coding, but were not reported by students and were subsequently removed.Thematic coding was verified by a second researcher who was blind to the initial coding. Inter-rater reliability was initially 84%, and remaining discrepancies were agreed upon by both researchers.

Clustering of learning hindrances

Hierarchical cluster analysis was used to determine whether there were any relationships betweenthe types of hindrances that students reported and the strategies they proposed to overcomethem. The analysis grouped hindrances together based on the frequency that each strategy wasreported for each hindrance. Infrequent hindrances reported by less than 5% of students were

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removed from the data set before analysis. Specifically, the clustering procedure used Ward’s clusteranalysis with chi-squared distance to form five clusters (Norušis 2012). The decision to stop at fiveclusters was based on the first substantial increase in the size of reported coefficients, and edu-cational relevance of the clusters. There were two clusters of behavioural hindrances (non-academiccommitments and academic commitments), two clusters of cognitive hindrances (difficulties under-standing concepts and difficulties concentrating in lectures) and one motivational cluster. These clus-ters align with self-regulated learning theories, which share the assumption that students havecontrol over their behaviours, cognitions and motivations during learning (Pintrich 2004).

Categorising students by academic performance

Subsets of consenting students were categorised as low-achieving, improving or high-achievingbased on their final grades in two first and two second year biomedical science courses. Coursegrades were calculated on a seven-point scale from 1–7 (1–3 = fail; 4 = pass; 5 = credit; 6 = distinction;7 = high distinction). High-achieving students (n = 22 in 2013; n = 28 in 2015) received distinctions orhigh distinctions across both years in all four courses. Low-achieving students (n = 7 in 2013; n = 8 in2015) failed at least one biomedical science course in first year and at least one in second year.Improving students (n = 16 in 2013; n = 5 in 2015) failed at least one biomedical science course infirst year, but passed both biomedical science courses in second year. Improving students were con-sidered to be resilient because they had suffered an academic hindrance (failure in first year), whichthey had overcome in their second year of study (Martin and Marsh 2009). These achievement groupswere used to identify differences in learning hindrances and strategies.

Student engagement with the learning management system

Engagement with the online learning management system at the beginning of semester was com-pared between high-achieving, low-achieving and improving students. The frequency of clicks on the‘learning resources’ content area was calculated to provide a broad measure of online engagement.This area was selected as it included all course lecture notes and lecture recordings, but did notinclude items related to assessment or discussion forums. A click was counted when studentsaccessed the learning resources content area, but not when accessing materials within it. Forexample, a student who accessed the learning resources content area then downloaded severalsets of lecture notes within a 24-hour period would only register as one click for that day. Thetotal number of clicks during the first three weeks of semester was calculated per student, which indi-cated engagement with the learning management system prior to the meta-learning questions onhindrances and strategies.

Statistical analysis

Statistical analyses were conducted with either Graphpad Prism® version 6 (San Diego, CA, USA) orIBM SPSS Statistics® version 22 (Somers, NY, USA). Significance was determined at p < .05. Allmeans are reported ± the standard error of the mean.

Ethics statement

This study was approved by the University of Queensland Behavioural & Social Sciences EthicalReview Committee (Approval number 2013000898). The consent rate for participation in the studywas 58% (n = 137) in 2013 and 35% (n = 88) in 2015. Fifteen consenting students did not completethe questions analysed in this study, resulting in a final number of 210 participants. For both 2013and 2015 offerings, the final course grade was compared between consenting students and thewhole cohort (2013: consenting = 78.2% ± 1.1, whole cohort = 75.5% ± 0.9; 2015: consenting =

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76.3% ± 1.5, whole cohort = 73.3% ± 0.9). Student’s t-tests demonstrated that the academic perform-ance of consenting students were not significantly different from the whole cohort in either 2013 (p= .07) or 2015 (p = .1), suggesting that the consenting students were representative of the wholecohort in each year. The average final course grade was not significantly different between 2013and 2015 (p = .1).

Results

Hindrances to learning

In either week 4 (2013) or 5 (2015) of semester, students (n = 210) were asked to report any hin-drances to their learning. Students often reported multiple hindrances, reporting on average 2 ± 1hindrances. Responses were coded inductively to identify the major types of hindrances (Table 1 &

Table 1. Hindrance classifications with corresponding examples of student responses.

Hindrance cluster Hindrance Examples of student responses

Cluster 1:Non-academichindrances

Lacking time Lack of time has been a hindrance for me because I have beenworking a lot and playing sport so I have very little free time.

Work commitments I work a part-time job at a pharmacy during the week as well asattending university, this takes up quite a bit of time and energy.

Social commitments General life distractions such as hanging with friends, work andhelping family all hinder learning to a certain degree.

Unspecified commitments I haven’t had as much time to go through the notes as often as I wouldlike to due to other commitments.

Falling behind in revisionor preparation

I have not been up to date as much as I would like to be and thismakes me nervous with our biom mid semester nearing.

Cluster 2:Motivational factors

Lack of motivation This week I was lacking motivation and took some time out formyself (too much time!).

Procrastination Procrastinating is the major thing that hindered my learning inBIOM2010 over the past few weeks. Even though I said to myselfI need to prioritize my learning before watching movies ordramas or doing activities that I shouldn’t be doing, I still don’tend up doing that.

Cluster 3:Academic commitments

Academic commitments The commencement of my community pharmacy placement hashindered my learning in BIOM2010. I have been busy doing allthe required modules prior to my placement, which includeswatching several videos and completing quizzes.

Content complexity The enormous volume of the material that we need to internalizeduring one semester and also a broad range of terminologies andmechanisms we are required to learn.

Cluster 4:Difficulties concentratingin lectures

Lecture time The time of the lectures. I really struggle when I’ve been at uni inthe morning, and studying for a couple hours in between lecturesto go to a 4 pm to 6 pm lecture. 2 hours straight that late in theafternoon isn’t overly helpful for my learning experience.

Tiredness Simply being tired during lectures, particularly the 8 am one. If I amtired then I know I don’t concentrate as much as I should, and have thepotential to miss important information.

Difficulties concentrating Sometimes I found it difficult to concentrate in lectures. Therefore, Isometimes missed what the lecturer had said during the lecture thenthis might cause me not to understand the following topics covered inthe lectures.

Cluster 5:Difficultiesunderstanding content

Difficulties understandingconcepts

A lack of understanding of a couple of concepts in the course,particularly signalling pathways have hindered my progress inlearning the content over the past few weeks.

Course materials Going back to lecture recording to write out all the notes aseverything that’s said in the lectures is not always on the printout sheets and a lot of information is given by the lecturer.

Missing or late to lecture Just the 8 am lectures – they’re hard to get to and by the time i get touni i usually miss it.

Notes: Student responses were inductively coded into hindrance themes. Students could report more than one hindrance, andsome quotes in the table include multiple hindrances. Bold indicates text that was coded to the relevant theme.

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Figure 1) with 19 types of hindrances mentioned. The most commonly reported was ‘academic com-mitments’, reported by 58 students (27.6%) who felt hindered by the workload associated with othercourses. Non-academic commitments were also frequently reported, including ‘work commitments’(43 students; 20.5%) and ‘social commitments’ (39 students; 18.6%). When all commitments (aca-demic, work, social and unspecified) were combined, 107 students (51%) experienced at least oneform of commitment as a hindrance to their learning.

Learning strategies

Students were also prompted to report the strategies that they could use in future to reduce theimpact of their learning hindrances. The strategies were coded deductively based on the self-regu-lated learning strategies outlined in Nota, Soresi, and Zimmerman (2004), with adaptations for uni-versity students (Table 2). Overall, students reported 1.5 ± 0.7 strategies. The most frequentlyreported strategies included ‘planning’ (89 students; 42.4%), ‘time management’ (61 students;29%) and ‘environmental structuring’ (48 students; 22.9%) (Figure 2).

Clustering of hindrances by strategies

Once the most common hindrances and strategies were identified, the relationship between themwas explored using a hierarchical cluster analysis. This grouped hindrances based on the types of

Figure 1. Hindrances to learning in BIOM2010. Students could report more than one hindrance.

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strategies students reported for each hindrance; thereby providing an indication of the most appro-priate strategies for each hindrance type. Five major clusters were identified after removing the hin-drances reported by less than 5% of students (Figure 3). Cluster 1 hindrances were reported by 98students, and focused on non-academic commitments. Cluster 2 was the smallest cluster (n = 44 stu-dents) and included hindrances focusing on motivational factors. Cluster 3 (n = 65 students) includedacademic hindrances associated with balancing study across courses and coping with the amount ortype of content. Cluster 4 (n = 63 students) centred on difficulties concentrating during lectures.Finally, cluster 5 (n = 48 students) focused on understanding course content. Students could reporthindrances from more than one cluster, with students reporting hindrances from 1.5 ± 0.7 clusterson average.

The strategies reported by students experiencing hindrances from each cluster differed, although‘planning’ was frequently reported for all clusters (Figure 4). Students who reported hindrances fromnon-academic or academic commitments clusters tended to report either ‘planning’ (49% and 55% ofstudents respectively) or ‘time management’ (46% and 40% of students respectively) as strategies.Students reporting motivational hindrances reported ‘environmental structuring’ and ‘planning’(45% and 43% of students respectively) strategies. When experiencing difficulties concentrating, stu-dents reported ‘planning’ and ‘health-related changes’ (37% and 32% of students respectively). Strat-egies for coping with difficulties understanding content were more diverse, and included ‘reviewingrecords’ (25%), ‘seeking information’ (23%), ‘planning’ (23%), ‘time management’ (21%) and ‘seekingsocial assistance’ (17%).

Table 2. Classification of strategies to overcome learning hindrances.

Category Description Examples from student responses

Goal setting Setting learning goals or remindingoneself of university or career goals.

Set small goals that are easily achieved so that can I slowlygain some motivation and enthusiasm again.

Planning Developing a study plan or planningahead for class.

Plan out my study work-load throughout the week. Byallocating time slots for study for each course it should,hopefully, allow for a more even distribution of myattention for each course.

Time management Organising time better, changingpriorities and reducing wasted time.

I think the most important thing I can do to improve my learning isworking effectively when necessary and not wasting time orprocrastinating on unnecessary things.

Self-consequences Motivation through rewards/punishment

Follow the plan and finish the work I set out to do, then rewardmyself with some entertainment.

Environmentalstructuring

Arrange the environment to enhancelearning and or reduce distractions.

Avoid using my computer during revising lectures along withavoiding other devices such as my mobile phone and Ipad.Additionally studying at the university library.

Keeping records Writing or typing notes, recordinglectures, printing lecture notes.

Actively take notes during the lecture to help me stay focuson the content. I make sure I write at least one or twosentences to summarize the content on each slide.

Attending lectures Student’s effort to attend lectures. The one thing I can control… is to attend lectures regularly.Reviewing records Revision of course material. For

example, Lecture notes or lecturerecordings

The one thing would be listening back to lecture recordings if Ido not fully understand the content or I forget to take notesin the lectures.

Seekinginformation

Seeking information from sources suchas textbooks or the internet.

I think to reduce the impact, I would have to look at more sourcesto confirm the correct concepts and find diagrams that i canunderstand more easily.

Seeking socialassistance

Seeking help from peers, lecturers orothers. Studying in groups

Another strategy is that i often study with a few friends inpharmacy which helps identify shortcomings or gaps inknowledge.

Transformingrecords

Changing learning materials.Integrating content between courses/contexts.

Try to relate the different subjects to each other and insteadof seeing them as individual subjects see the course as onebig subject with different parts to it

Health-relatedchanges

Attempts to be healthier. For example,get more sleep or eat breakfast.

I think breakfast is very important before morning lecturesnot to just say to yourself ‘I’ll eat after’ because you need theenergy in the lecture to focus.

Notes: Student responses were deductively coded against the self-regulation learning strategies adapted from Nota, Soresi, andZimmerman (2004). Bold indicates text that was coded to the relevant theme.

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Differences in hindrances and strategies across achievement groups

Students were categorised as high-achieving (n = 50), improving (n = 21) or low-achieving (n = 15)based on their performance in first and second year biomedical science courses. The hindrance clus-ters and strategies were compared between these achievement groups. As the number of studentswithin each group was different, the results were normalised as the percentage of students from eachgroup reporting hindrances within each cluster or strategy. High-achieving students most frequentlyreported non-academic commitments (48%) and difficulties concentrating in lectures (38%) as hin-drances (Figure 5), which is consistent with their frequent report of ‘planning’ (44%), ‘time manage-ment’ (28%) and ‘environmental structuring’ (30%) as strategies (Figure 6). Low-achieving studentsfrequently reported both non-academic (40%) and academic commitments (47%) as hindrances,and therefore relied heavily on ‘planning’ (60%) and time management (27%). Interestingly, low-achieving students were the least likely to report motivational factors (7%) or difficulties understand-ing content (13%) as hindrances and therefore infrequently reported motivational strategies such as‘environmental structuring’ (7%), or strategies for improving understanding such as ‘reviewingrecords’ (7%), ‘seeking information’ (0%) or ‘seeking social assistance’ (0%). Finally, improving stu-dents were most likely to report difficulties understanding content (38%) and the least likely to

Figure 2. Strategies reported by students to overcome learning hindrances. Students could report more than one strategy. Responseswere coded into self-regulated learning strategy categories adapted from Nota, Soresi, and Zimmerman (2004).

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report non-academic commitments (24%) and difficulties concentrating in lectures (24%) ashindrances. Therefore, strategies reported by improving students focused on obtaining andunderstanding information (‘reviewing records’ = 19%, ‘seeking information’ = 19% and ‘seekingsocial assistance’ = 14%), although they also relied on ‘planning’ (38%) and ‘environmentalstructuring’ (24%).

There are many possible reasons why the low-achieving students infrequently reported motiva-tional factors and difficulties understanding content as hindrances. One possibility is that perhapsthese students were less engaged with the course. To test this hypothesis, the frequency withwhich students clicked on the ‘learning resources’ area within the learning management systemwas compared between achievement groups after the first three weeks of semester. However, theaverage number of clicks on learning resources did not significantly differ between groups duringthe three weeks, with an average number of 8.4 ± 0.7 clicks per three weeks for high-achieving stu-dents, 9.7 ± 1.8 for improving students and 9.7 ± 2.6 for low-achieving students. Nor were there anysignificant differences across the entire semester (high-achieving = 41 ± 2.5; improving = 46.6 ± 5.8;low-achieving = 38.7 ± 6.8).

Discussion

This study had two primary aims: (1) to identify and categorise hindrances faced by second yearundergraduate students studying biomedical sciences and the self-regulated learning strategiescommonly used to overcome these hindrances; and (2) to determine how improving students dif-fered in their hindrances and strategies from other student subsets in order to identify possible learn-ing traits associated with academic resilience. In this study, we conceptualised resilient students as

Figure 3. Dendrogram of hindrances clustered by learning strategies. In the dendrogram, vertical lines indicate hindrances that clus-tered together and horizontal lines indicate the degree of similarity between hindrances based on the frequency of reported strat-egies. The smaller the horizontal line from left to right, the more similar the hindrances are. Clusters are indicated by the numberson the left.

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those who had overcome an academic hindrance (course failure) in first year by demonstratingimproved academic performance (passing) in second year (Martin and Marsh 2009). However, it isimportant to acknowledge that there are many reasons why students may fail in their first year, orcontinue to fail during subsequent years (Killen 1994). Indeed, remaining at university despite aca-demic failure is one possible form of resilience. Resilience is multi-factorial (Morales 2008b) andthis study aims to measure one facet of this complex phenomenon.

Figure 4. Strategies reported by students to deal with hindrances. The frequency of strategies reported for each cluster is presentedas the percentage of students in each cluster reporting that strategy. Each student could report hindrances from more than onecluster, and could report more than one strategy within or across clusters. Cluster 1: non-academic commitments (n = 98); cluster 2:motivational factors (n = 44); cluster 3: academic commitments (n = 65); cluster 4: difficulties concentrating in lectures (n = 63) andcluster 5: difficulties understanding content (n = 48).

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Students reported a broad range of hindrances (Figure 1). Over half reported feeling hindered byacademic, work, social or unspecified commitments, with ‘academic commitments’ being the mostfrequently reported. This finding, together with the frequent reports of ‘lacking time’ and ‘fallingbehind in revision or preparation’, suggests that students were struggling with their work-life-study balance. Students also said they had difficulty concentrating, citing the lecture time as variouslytoo early, too late or too long. It is important to acknowledge that the hindrances experienced bystudents were captured at a single time point early in semester, and these hindrances maychange as the semester progresses. For example, it is likely that academic commitments wouldbecome more frequent as students are faced with increasing levels of assessment.

The types of hindrances students reported are those that are likely to be experienced day-to-day,often referred to as ‘daily hassles’ (Kanner et al. 1981) in the psychological literature. Interestingly,although seemingly minor, similar types of hindrances have been reported as factors influencing astudent’s intention or decision to withdraw from university, suggesting that these everyday hin-drances can have serious consequences. Within the Australian context, in a large-scale studyKrause (2005) identified the characteristics of first year students who were seriously considering with-drawing from university. In line with our current findings on hindrances, the authors characterisedsuch students as those who spent less time on campus and in class, and more time in paid work,who had difficulty understanding course material, and felt overwhelmed by university study. Theyspent less than average time on study, often came unprepared to class, and experienced difficultygetting motivated and adjusting to university teaching styles (Krause 2005). In a UK study of studentswho had withdrawn from university after their first year, Yorke and Longden (2008) identified sevenfactors that influenced a student’s decision to withdraw. Two of these factors overlap with the hin-drances reported in our own study: specifically, difficulties coping with academic demand (including

Figure 5. Hindrance clusters reported by different achievement groups. Bars represent the percentage of students categorised ashigh-achieving (diagonal stripes), low-achieving (dots) and improving (checkers). Students could report hindrances from morethan one cluster.

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heavy workload and stress related to study) and problems with finance and employment. Similarly,Long, Ferrier, and Heagney (2006) identified reasons for attrition of students who had withdrawnafter their first year at university, and of particular relevance to our findings, reported that themain reason for attrition was difficulty balancing work and study (Long, Ferrier, and Heagney2006). Other relevant factors included stress and anxiety related to study, inadequate preparationfor study, clashes with family commitments and illness. Taken in conjunction with the literature,our results suggest that many students at university experience the same types of everyday hin-drances to learning that may lead to attrition. Potentially, such everyday hindrances might only

Figure 6. Strategies reported by different achievement groups. High-achieving (diagonal stripes; n = 50), low-achieving (dots; n = 15)and improving (checkers; n = 21) students were categorised based on their final grades in biomedical science courses across twoyears. Bars represent the proportion of students reporting each strategy within each achievement group as a percentage of thetotal number of students within that group. Students could report more than one strategy.

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lead to attrition if they are perceived to be significant barriers, if the hindrances compound or occur inconjunction with other factors that are commonly associated with attrition, such as a lack of academicand social integration (Kantanis 2000; Tinto 2010).

When the hindrances students faced were considered together with the strategies they planned touse to overcome them, five clusters were apparent (Figure 3). The hindrances clustered as non-aca-demic commitments; motivational factors; academic commitments; difficulties concentrating in lec-tures and difficulties understanding content. When dealing with academic hindrances, manystudents said they intended to plan more and manage their time better, with these strategies beingranked in the top four across all hindrance clusters (Figure 4). Both these strategies describe effortsto organise time more efficiently. However, these skills are difficult to master and are often under-developed in tertiary students, particularly within their first year (Bonestroo and de Jong 2012; vander Meer, Jansen, and Torenbeek 2010). Establishing an effective study plan is meta-cognitively complex as it involves understanding the requirements of a learning task and theknowledge or processes that must be learnt during that task, as well as accurate evaluation of one’scurrent level of knowledge (Bonestroo and de Jong 2012; Winne 1995). Research into planning andtime management at university is fairly limited (Bonestroo and de Jong 2012); however, there issome evidence that both of these processes correlate with academic success. Bonestroo and deJong (2012) compared computer-based learning tools for planning, and found that students whowere actively involved in generating learning plans gained more structural knowledge than studentswho were passively involved. Time management has also been positively correlated with academicperformance at university (Gortner Lahmers and Zulauf 2000; Macan et al. 1990) and negatively corre-lated with stress (Macan et al. 1990). Given the frequency that time management and planningappeared in our clusters, efforts to improve these skills could have an impact on a broad range of learn-ing hindrances. That some students struggle to transition to independent learning and identify howthey should be using their free time at university (van der Meer, Jansen, and Torenbeek 2010) suggeststhat simple interventions to keep students regularly engaged with study may have a profound effect,such as providing formative quizzes, scheduling tutorials or introducing active learning activitieswithinthe classroom (Ernst and Colthorpe 2007; Nicol and Macfarlane-Dick 2006; Prince 2004).

Other strategies commonly reported can be broadly defined as those aimed at improvingmotivation or the acquisition and understanding of knowledge (Sandars and Cleary 2011;Wolters 2003; Zimmerman 2000). Wolters (2003) identified a number of strategies to sustainmotivation to study, and our students reported many such strategies when faced with motiva-tional hindrances (Figure 4). These included changing their environment, creating self-conse-quences and setting goals, with ‘environmental structuring’ being the most frequently reportedstrategy. This strategy includes efforts to control the environment to avoid distraction andsustain effort (Nota, Soresi, and Zimmerman 2004; Zimmerman and Martinez-Pons 1988). Interest-ingly, ‘environmental structuring’ was reported more frequently for non-academic commitmentscompared to academic commitments. This suggests that external commitments, such as work,sport or social engagements, may be associated with difficulties sustaining motivation at univer-sity. Although previous studies have used a broader definition of environmental structuring,including strategies to sustain attention through changes to mental and physical health (Purdieand Hattie 1996; Wolters 2003), we classified these types of strategies separately as ‘health-related changes’. Our students considered these most useful when faced with difficulties concen-trating in lectures.

Strategies aimed at acquiring or understanding knowledge were most often reported for hin-drances clustered as difficulties understanding content (Figure 4). These strategies included ‘review-ing records’, ‘seeking information’ and ‘seeking social assistance’, which were reported mostfrequently by improving students (Figure 6). In contrast, only 7% of low-achieving students reported‘reviewing records’ as a strategy to overcome hindrances, and they did not mention the latter strat-egies at all. These differences in reported strategies corresponded to differences in their reported hin-drances (Figure 5). Specifically, improving students commonly reported difficulties understanding

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content as a hindrance, whereas low-achieving students frequently reported hindrances from theacademic and non-academic commitments clusters, suggesting that these students were strugglingto balance study with other time commitments. Given this finding, it was thought possible that low-achieving students would not have engaged with the course content. However, these studentsaccessed the course learning resources as frequently as high-achieving or improving students,suggesting that they were making some effort to keep up with course content. It is possible thatthe low-achieving students were less aware of the demands of the course and therefore did not per-ceive their current level of understanding as a hindrance. Although the low-achieving students wereaccessing the learning resources, it is also possible that they were not monitoring or evaluating theirunderstanding of those resources, and subsequently did not recognise difficulties understanding as alearning hindrance.

Both self-monitoring and self-evaluation are important self-regulatory processes, and involve criti-cally appraising one’s comprehension of learningmaterial and the effectiveness of the learning processeither during (self-monitoring) or after a learning task (self-evaluation) (Kitsantas and Dabbagh 2009).Low-achieving students have been shown to consistently overestimate their performance on examin-ations (Dunning et al. 2003; Pazicni and Bauer 2014), suggesting that they struggle to accurately evalu-ate their understanding, which may be related to poorly developed self-monitoring (Boekaerts andRozendaal 2010; Winne 1995). In contrast, the improving students in our study were the least likelyto report ‘non-academic commitments’ as hindrances, suggesting they may have reduced their exter-nal time commitments to prioritise university study. Although the sample sizes for improving and low-achieving students were small, these results tentatively suggest that one important factor for encoura-ging academic resilience (as measured by an improvement in grades) may be the ability to prioritisestudy and monitor understanding of course content in a timely fashion.

Implications for educational design

Students experience a variety of hindrances to their learning, and it is important that educationaldesigners provide students with formal opportunities to recognise and minimise the impact ofthese hindrances to avoid failure and possible attrition (Krause 2005; Tinto 2010; Yorke andLongden 2008). The current study captures the learning hindrances of a cohort of second year under-graduate biomedical sciences students prior to the majority of their summative assessment. Studentsexperienced a variety of hindrances, some of which could be easily avoided in a well-designed course.For example, early or late lecture slots could be avoided where possible, or active learning strategies(Ernst and Colthorpe 2007; Prince 2004) could be used to prevent difficulties concentrating duringlectures. In addition, course assessment could be mapped against concurrent courses to minimisehindrances associated with academic commitments. In the current study, improving and low-achiev-ing students reported clear differences in hindrances and strategies. In particular, low-achieving stu-dents were less likely to report difficulties understanding content or strategies that would improvetheir understanding. Therefore, all students, but particularly struggling students, may benefit fromembedded opportunities for early reflection on content mastery. A variety of educational interven-tions would be effective for achieving this aim, including formative assessment with feedback(Nicol and Macfarlane-Dick 2006) and interventions aimed at improving metacognition (Tanner2012; van den Boom, Paas, and van Merriënboer 2007). These interventions would be most beneficialwhen implemented early and frequently (Tinto 2010), with an emphasis on self-evaluation. Throughthese scaffolded experiences, students may acquire the skills to self-regulate their learning, and trans-form into resilient learners.

Acknowledgements

This manuscript was prepared as part of the Biology Scholars Program (Transitions Residency) organised by the AmericanSociety for Microbiology. The authors wish to thank the organisers and participants for input and feedback.

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Disclosure statement

No potential conflict of interest was reported by the authors.

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