Fellow of the Higher Education Academy (FHEA)
I thought it may be useful to others for me to share my application materials for my FHEA application. I applied through the Cardiff University Education Fellowships Programme. This application was successful in June 2023.
Context
My background is in ecology but my role at Cardiff University involves teaching biostatistics. Specifically, I am employed as a Tutor in Data Analysis and Statistics, on a Teaching and Scholarship pathway. I have teaching and scholarship interests and responsibilities in movement ecology, statistics, and biological education and pedagogy. My background includes substantial teaching experience in ecology and statistics, as both a Demonstrator and Lecturer at Swansea University (for 6 years).
I design and deliver teaching to all Y1 and Y2 undergraduates (UG) (480 and 440 students, respectively), three Master’s level degree programmes (approx. 90 students) and the SWBio course (40-50 PhD students). The teaching format typically consists of week-long intensive courses comprising lectures and computer-based practicals. I also lead sessions for Y2 UG laboratory practicals (45-90 students); field-based practicals in marine biology and movement ecology–single-day sessions and residential field-courses–at Y2 UG and MSc level (~30 students); and deliver statistics training to students on Week-Long Research Experience placements (~90 students).
I also provide statistics support (through bespoke workshops, data clinics, and 1-to-1 sessions) to the entire School of Biosciences (1764 undergraduate students; 367 postgraduate students; 127 academic staff). I also supervise Y3 UG, MSc/MRes, and PhD projects.
I design and deliver teaching to all Y1 and Y2 undergraduates (UG) (480 and 440 students, respectively), three Master’s level degree programmes (approx. 90 students) and the SWBio course (40-50 PhD students). The teaching format typically consists of week-long intensive courses comprising lectures and computer-based practicals. I also lead sessions for Y2 UG laboratory practicals (45-90 students); field-based practicals in marine biology and movement ecology–single-day sessions and residential field-courses–at Y2 UG and MSc level (~30 students); and deliver statistics training to students on Week-Long Research Experience placements (~90 students).
I also provide statistics support (through bespoke workshops, data clinics, and 1-to-1 sessions) to the entire School of Biosciences (1764 undergraduate students; 367 postgraduate students; 127 academic staff). I also supervise Y3 UG, MSc/MRes, and PhD projects.
Reflective Blog 1: Reflection on the UKPSF and identifying your teaching philosophy and areas of focus
My experiences of learning statistics comprise bad memories of enforced passive-learning; lectures with information I didn’t understand; not having enough time, space, or encouragement to comprehend the subject matter and instead having to remember facts. I was awful at statistics and for years suffered from “statistics anxiety” (Trassi et al., 2022) [K1].
“Eureka!” – Archimedes (supposedly!)
Over the course of my PhD this changed, thanks to one very patient, empathetic, and inspirational supervisor who understood my method of learning and tailor their teaching to suit my deep-learning style. This, together with the time and freedom to conduct self-study, I facilitated my own Eureka! moments (Knoblich & Oellinger, 2006), began to understand and appreciate statistics, and developed a passion for it. I’m convinced that my journey from loathing to loving statistics played a role in defining my teaching philosophy: “All students are unique, must have an inspiring and compassionate environment in which to learn, with teaching suited to individual needs and abilities, enabled by tailored support [A4, V1]. Together these create an environment that facilitates learning and motivates both the desire to learn and a sense of purpose through learning, thus promoting learning autonomy (n.b. “Timeless Learning”; Miller, 2005) [V3]. I believe this will ultimately develop students into independent and passionate learners responsible for their own learning and direction – this is my aim as an educator.”
I feel that this teaching philosophy ultimately drives my approach which I see as towards the “learner-focused” end of the continuum of learning approaches (Samuelowicz and Bain, 2001) [A5, K1, V3]; specifically, I see my role as being to support and facilitate students’ learning (Larsson, 1983; Samuelowicz and Bain, 1992) and cultivate intellect and growth (Fox 1983; Kember & Kwan, 2000). Part of my method to achieve this is to approach teaching as my supervisor always did; in a compassionate manner to foremostly support a students’ psychological needs (Maslow, 1943) [A2, A4, K1-2, V1-3]. This is exemplified by the following student feedback: “Dr Kay was really helpful and friendly, and really took the time to make sure I understood what I wanted to, he showed me some new software to help with my calculations and was really patient.”
“When you read, don’t just consider what the author thinks, consider what you think” – John Keating, Dead Poets Society
I have also been inspired by approach demonstrated by the fictional John Keating of Tom Schulman’s Dead Poets Society [A5]. For me, Keating embodies the values, knowledge, and practices that make a teacher great: he is passionate, compassionate, and inspirational; he encourages his students to be critical; he promotes participation; he strives to understand how students learn; and he uses sometimes unorthodox approaches to make learning effective. Contrary to the traditional methods used at Welton Academy, Keating uses an active learning approach (Felder and Brent, 2009) [K1] by encouraging his students to think for themselves, be creative, and find ways to develop understanding independently.
I strive to use active learning methods in my teaching because evidence suggests they are superior to traditional approaches (Deslauriers et al., 2019) [A5, K1]. I have found that active learning is well received by students studying statistics, for example by providing a platform for them to discuss their queries [A4]. I facilitate this regularly at Data Clinics, which makes effective use of the zone of proximal development (Wertsch, 1984) [A4, A5, K1, K3, V3]. One student provided the following written feedback [K5]: “It was great to be able to drop in and discuss a statistical question! I always find it difficult to find the answer online, but being able to discuss the query was very helpful.”
I often make use of Socratic questioning (Paul and Elder, 2019) to encourage students to think for themselves [A2, K2, K4] and in-doing-so solve their own queries [K3], which develops confidence [V4]. I feel that active learning is however not without consequence; it has been demonstrated that while this approach can greatly benefit some (Carlson & Winquist, 2011), it can hinder others (Weltman & Whiteside, 2010) [V1, K1]. Thus, it isn’t always the most inclusive way to teach (Lindsay, 2003). This may because this method is perceived to be more effective for extroverts who are willing to discover information on their own (Monahan, 2013); although recent research suggests introverts are not disadvantaged (Flanagan & Addy, 2019). In my experience active learning can sometimes be disregarded by students who are conducting surface-learning, and tend to be more effective for students embracing a deep-learning approach (Mizokami, 2018) [K1-K3].
“Eureka!” – Archimedes (supposedly!)
Over the course of my PhD this changed, thanks to one very patient, empathetic, and inspirational supervisor who understood my method of learning and tailor their teaching to suit my deep-learning style. This, together with the time and freedom to conduct self-study, I facilitated my own Eureka! moments (Knoblich & Oellinger, 2006), began to understand and appreciate statistics, and developed a passion for it. I’m convinced that my journey from loathing to loving statistics played a role in defining my teaching philosophy: “All students are unique, must have an inspiring and compassionate environment in which to learn, with teaching suited to individual needs and abilities, enabled by tailored support [A4, V1]. Together these create an environment that facilitates learning and motivates both the desire to learn and a sense of purpose through learning, thus promoting learning autonomy (n.b. “Timeless Learning”; Miller, 2005) [V3]. I believe this will ultimately develop students into independent and passionate learners responsible for their own learning and direction – this is my aim as an educator.”
I feel that this teaching philosophy ultimately drives my approach which I see as towards the “learner-focused” end of the continuum of learning approaches (Samuelowicz and Bain, 2001) [A5, K1, V3]; specifically, I see my role as being to support and facilitate students’ learning (Larsson, 1983; Samuelowicz and Bain, 1992) and cultivate intellect and growth (Fox 1983; Kember & Kwan, 2000). Part of my method to achieve this is to approach teaching as my supervisor always did; in a compassionate manner to foremostly support a students’ psychological needs (Maslow, 1943) [A2, A4, K1-2, V1-3]. This is exemplified by the following student feedback: “Dr Kay was really helpful and friendly, and really took the time to make sure I understood what I wanted to, he showed me some new software to help with my calculations and was really patient.”
“When you read, don’t just consider what the author thinks, consider what you think” – John Keating, Dead Poets Society
I have also been inspired by approach demonstrated by the fictional John Keating of Tom Schulman’s Dead Poets Society [A5]. For me, Keating embodies the values, knowledge, and practices that make a teacher great: he is passionate, compassionate, and inspirational; he encourages his students to be critical; he promotes participation; he strives to understand how students learn; and he uses sometimes unorthodox approaches to make learning effective. Contrary to the traditional methods used at Welton Academy, Keating uses an active learning approach (Felder and Brent, 2009) [K1] by encouraging his students to think for themselves, be creative, and find ways to develop understanding independently.
I strive to use active learning methods in my teaching because evidence suggests they are superior to traditional approaches (Deslauriers et al., 2019) [A5, K1]. I have found that active learning is well received by students studying statistics, for example by providing a platform for them to discuss their queries [A4]. I facilitate this regularly at Data Clinics, which makes effective use of the zone of proximal development (Wertsch, 1984) [A4, A5, K1, K3, V3]. One student provided the following written feedback [K5]: “It was great to be able to drop in and discuss a statistical question! I always find it difficult to find the answer online, but being able to discuss the query was very helpful.”
I often make use of Socratic questioning (Paul and Elder, 2019) to encourage students to think for themselves [A2, K2, K4] and in-doing-so solve their own queries [K3], which develops confidence [V4]. I feel that active learning is however not without consequence; it has been demonstrated that while this approach can greatly benefit some (Carlson & Winquist, 2011), it can hinder others (Weltman & Whiteside, 2010) [V1, K1]. Thus, it isn’t always the most inclusive way to teach (Lindsay, 2003). This may because this method is perceived to be more effective for extroverts who are willing to discover information on their own (Monahan, 2013); although recent research suggests introverts are not disadvantaged (Flanagan & Addy, 2019). In my experience active learning can sometimes be disregarded by students who are conducting surface-learning, and tend to be more effective for students embracing a deep-learning approach (Mizokami, 2018) [K1-K3].
Reflective Blog 2: Application of educational theories
I feel that statistics can be made much more difficult than necessary, such as where students get concerned with trying to remember the multitude of statistical tests that exist and the scientific questions where each is applied [K1]; a task that is as impossible as it is unnecessary. Nevertheless, educators have developed tools to help students remember statistical tests (such as mnemonics (Stalder & Olson, 2011) or song; Gesler, in prep) [K1]. I feel that encouraging students to wrote-learn is a poor approach, not least because it promotes development of only basic levels of ability (n.b. Bloom’s Taxonomy; Bloom et al., 1956) [K3]. Rather, I think the best way for students to learn is to teach them to understand, analyze, and evaluate why tests are applied, highlighting how this usually depends on their distribution of data [K1]. To teach this, I use simple but effective visualization tools – such as Microsoft Paint [K4] – to draw distributions, represent data in different ways, and visualise hypotheses [A1, A2, K2]; understanding these helps students to determine why a particular test is needed [K3], exemplified by the following feedback: “Will explained my query in detail and helped me to understand fully why I should use a specific statistical test”
I achieve the above through a social-constructivist approach (Kalina & Powell, 2009), facilitating students to learn collaboratively with me (Bruffee, 1993) [K3], for example by encouraging them to think about the shape(s) to draw, given their data distribution(s) [A1]; a method that research has been shown to be effective (Bakker & Hoffman, 2005) [A2, K2, V3] and that I feel works well; “Dr Kay, myself, and another student had a very constructive interactive session allowing us to interpret our results from our statistical analysis”. By teaching like this, my aim is to stimulate interactions to facilitate learning [A2, K2]; I believe that supporting students to generate their own understanding develops a sense of accomplishment, builds confidence, and promotes participation [V2]. Encouragement is crucial to overcoming statistics anxiety (Wilson, 1999) and I feel that this approach engenders a growth (rather than fixed) mindset (Dwek, 2006).
I am aware that not all students learn well collaboratively, nor enjoy this approach, and I worry that I don’t always provide all students with equitable opportunities to learn [V1]. This is sometimes apparent during teaching (for example a student not engaging [A3]); as someone who enjoys teaching like this, I can find this dissatisfying. In these situations I sometimes signpost students to online resources – such as the Statspack – to conduct self-regulated learning (Boekaerts, 1999) [K3, V1]. I’ve found this serves well for students conducting surface learning in preparation for an upcoming assessment [K3]. However, research indicates surface learning is ineffective for understanding statistics long-term (Garfield & Ben-Zvi, 2007; Diamond, 2011) [K1, V3] and so I often feel I’ve failed as an educator if a student leaves a clinic having only “found out the answer”, rather understood it.
I structure statistics classes so that lectures are immediately followed by computer-based practicals [A1], as recommended by colleagues (Medeiros et al., 2023) [V3]. I feel this is successful largely because the time elapsed between learning concepts and applying them is minimal. Feedback from students following this year’s statistics course highlights this: “I think the short lecture/practical format worked incredibly well and helped contextualise the information” and “…explaining general concepts before applying them made it easier to understand what we were doing”. In these practicals I implement formal and informal learning activities (Malcolm et al., 2003). Firstly, after explaining new concepts I provide students with a practice coding script to execute a novel task. In this formal element I often guide students through sections of code [A2], scaffolding this with formative questions that students answer in parallel (Bruner, 1978) [A2, K2, K3]. For the non-formal activity, I provide students with new data to explore perform an analysis independently [A2]. I feel that most students find this approach effective, however I’m concerned that the overall structure of the courses I teach on undermines this. Short, intensive statistics courses have been shown to encourage surface learning (Diamond, 2011) [K1], which does not facilitate development of higher levels of learning (Bloom et al., 1956) [K3]. In future, I will trial restructuring these courses into more typical, semester-long formats which I feel aligns better with the wider UK HE sector [K6, V3]. The School of Biosciences is preparing for revalidation, and I have championed this idea as part of this process [A5, V4].
I achieve the above through a social-constructivist approach (Kalina & Powell, 2009), facilitating students to learn collaboratively with me (Bruffee, 1993) [K3], for example by encouraging them to think about the shape(s) to draw, given their data distribution(s) [A1]; a method that research has been shown to be effective (Bakker & Hoffman, 2005) [A2, K2, V3] and that I feel works well; “Dr Kay, myself, and another student had a very constructive interactive session allowing us to interpret our results from our statistical analysis”. By teaching like this, my aim is to stimulate interactions to facilitate learning [A2, K2]; I believe that supporting students to generate their own understanding develops a sense of accomplishment, builds confidence, and promotes participation [V2]. Encouragement is crucial to overcoming statistics anxiety (Wilson, 1999) and I feel that this approach engenders a growth (rather than fixed) mindset (Dwek, 2006).
I am aware that not all students learn well collaboratively, nor enjoy this approach, and I worry that I don’t always provide all students with equitable opportunities to learn [V1]. This is sometimes apparent during teaching (for example a student not engaging [A3]); as someone who enjoys teaching like this, I can find this dissatisfying. In these situations I sometimes signpost students to online resources – such as the Statspack – to conduct self-regulated learning (Boekaerts, 1999) [K3, V1]. I’ve found this serves well for students conducting surface learning in preparation for an upcoming assessment [K3]. However, research indicates surface learning is ineffective for understanding statistics long-term (Garfield & Ben-Zvi, 2007; Diamond, 2011) [K1, V3] and so I often feel I’ve failed as an educator if a student leaves a clinic having only “found out the answer”, rather understood it.
I structure statistics classes so that lectures are immediately followed by computer-based practicals [A1], as recommended by colleagues (Medeiros et al., 2023) [V3]. I feel this is successful largely because the time elapsed between learning concepts and applying them is minimal. Feedback from students following this year’s statistics course highlights this: “I think the short lecture/practical format worked incredibly well and helped contextualise the information” and “…explaining general concepts before applying them made it easier to understand what we were doing”. In these practicals I implement formal and informal learning activities (Malcolm et al., 2003). Firstly, after explaining new concepts I provide students with a practice coding script to execute a novel task. In this formal element I often guide students through sections of code [A2], scaffolding this with formative questions that students answer in parallel (Bruner, 1978) [A2, K2, K3]. For the non-formal activity, I provide students with new data to explore perform an analysis independently [A2]. I feel that most students find this approach effective, however I’m concerned that the overall structure of the courses I teach on undermines this. Short, intensive statistics courses have been shown to encourage surface learning (Diamond, 2011) [K1], which does not facilitate development of higher levels of learning (Bloom et al., 1956) [K3]. In future, I will trial restructuring these courses into more typical, semester-long formats which I feel aligns better with the wider UK HE sector [K6, V3]. The School of Biosciences is preparing for revalidation, and I have championed this idea as part of this process [A5, V4].
Reflective Blog 3: Effective teaching practice
Learning Objectives (LOs) are fundamental building blocks of teaching design (Linder, 2017), including for inclusivity (Peters, 2004) [K1]. I begin my teaching sessions by defining LOs [V3]. This outcome-focused approach better supports a diverse student body than a content focused one by making the goals clear (Wiggins & McTighe, 1998) [V1]; it also helps me to plan sessions to achieve these objectives (Petty, 2009) [K1, K2]. Designing teaching in this way facilitates constructive alignment, by ensuring that LOs, materials, and assessments are linked (Biggs, 1996). It also allows me to review the aims with students midway through activities [A1, A2], which I feel helps both parties to maintain focus [A4, V2].
When teaching statistics, a highly pervasive inclusivity issue is “statistics anxiety”; defined as “the apprehension that occurs when an individual is exposed to statistics” (Macher et al., 2015) [K1]. Research shows that embedding historical references (Langan, 2023) or something lighthearted (Schacht & Stewart, 1990) into teaching mitigates this [K1]. I have trialled the former [A2], and anecdotal feedback suggests this is positive. I have also introduced lighthearted elements, for example by introducing students to my pets at Data Clinics [A2]. I tried this because research has shown that the presence of animals reduces stress and anxiety (Friedman & Son, 2009) [V3]. I found that it really helped to break down barriers and build community, and made learning statistics a fun and memorable experience [A2, A4, K2, K4, V2]. In fact, I’m convinced this contributes to constructive alignment by making students more comfortable being communicative, which ultimately makes them more engaged [V2]. In future I aim to more formally assess whether this is beneficial for mitigating statistics anxiety [A5].
I strive to provide multiple means of engagement in my teaching by including relevant and authentic examples and linking this to student experience. A key example of this is in having embedded live statistics tuition into a Y2 laboratory practical [A1, A5]. Specifically in a laboratory experiment students generate real biological data which they bring to me [V2]. I then demonstrate how to inspect and analyse the data using R [K2, K4]. This provides a personalised and authentic learning opportunity [A4, V1]. I feel that this approach facilitates an inclusive and collaborative learning environment, generates high levels of motivation, and supports the development of high-level learning domains (n.b. Bloom’s Taxonomy; Bloom et al., 1956) [A4, K3]. This was reinforced in feedback received from colleagues: “… the students were following your explanations and interpretation; the vast majority were fully engaged.”
I also aim to offer multiple means of representation by providing learning materials in a written form (3 days before sessions) and videos with transcripts (recordings with 24 hours of live sessions) [A1, A2, K4, V1, V2]. I feel this supports different learning styles and assists students with challenges such as having English as a second language or specific learning needs (e.g., dyslexia). Something that I have relatively little experience in using is a flipped-learning approach. I understand that this can be beneficial from an inclusivity perspective, especially for students with learning difficulties (Altemueller & Lindquist, 2017), and I plan to use this more in future [A5].
When teaching online I use Slack – an accessible online communication platform – to interact with students, address questions quickly and efficiently, and build a collaborative social environment [K4, V1, V2]. However, in addition to this, students must use R, Zoom, and Möbius. An issue I’ve found with this is overloading my audience. For example, of 52 students that provided feedback, 19 said that accessing Möbius alongside everything else was a “stressful extra”, and 14 opted out of using it [K5]. I don’t think these platforms aren’t useful; rather, I think the opportunity that students have to use them is inadequate [K4]. I feel this is because of the severely limited temporal environment of the current teaching format of short, intensive courses [K6]. This is compounded by occasional technological issues, such as a student’s computer malfunctioning. I find issues like these frustrating because they result in a poor student experience; students typically cannot complete the tasks and rarely have time to catch up. As outlined in Blog 2, I intend to improve the course format as part of the School’s upcoming revalidation [A5, K6, V3].
When teaching statistics, a highly pervasive inclusivity issue is “statistics anxiety”; defined as “the apprehension that occurs when an individual is exposed to statistics” (Macher et al., 2015) [K1]. Research shows that embedding historical references (Langan, 2023) or something lighthearted (Schacht & Stewart, 1990) into teaching mitigates this [K1]. I have trialled the former [A2], and anecdotal feedback suggests this is positive. I have also introduced lighthearted elements, for example by introducing students to my pets at Data Clinics [A2]. I tried this because research has shown that the presence of animals reduces stress and anxiety (Friedman & Son, 2009) [V3]. I found that it really helped to break down barriers and build community, and made learning statistics a fun and memorable experience [A2, A4, K2, K4, V2]. In fact, I’m convinced this contributes to constructive alignment by making students more comfortable being communicative, which ultimately makes them more engaged [V2]. In future I aim to more formally assess whether this is beneficial for mitigating statistics anxiety [A5].
I strive to provide multiple means of engagement in my teaching by including relevant and authentic examples and linking this to student experience. A key example of this is in having embedded live statistics tuition into a Y2 laboratory practical [A1, A5]. Specifically in a laboratory experiment students generate real biological data which they bring to me [V2]. I then demonstrate how to inspect and analyse the data using R [K2, K4]. This provides a personalised and authentic learning opportunity [A4, V1]. I feel that this approach facilitates an inclusive and collaborative learning environment, generates high levels of motivation, and supports the development of high-level learning domains (n.b. Bloom’s Taxonomy; Bloom et al., 1956) [A4, K3]. This was reinforced in feedback received from colleagues: “… the students were following your explanations and interpretation; the vast majority were fully engaged.”
I also aim to offer multiple means of representation by providing learning materials in a written form (3 days before sessions) and videos with transcripts (recordings with 24 hours of live sessions) [A1, A2, K4, V1, V2]. I feel this supports different learning styles and assists students with challenges such as having English as a second language or specific learning needs (e.g., dyslexia). Something that I have relatively little experience in using is a flipped-learning approach. I understand that this can be beneficial from an inclusivity perspective, especially for students with learning difficulties (Altemueller & Lindquist, 2017), and I plan to use this more in future [A5].
When teaching online I use Slack – an accessible online communication platform – to interact with students, address questions quickly and efficiently, and build a collaborative social environment [K4, V1, V2]. However, in addition to this, students must use R, Zoom, and Möbius. An issue I’ve found with this is overloading my audience. For example, of 52 students that provided feedback, 19 said that accessing Möbius alongside everything else was a “stressful extra”, and 14 opted out of using it [K5]. I don’t think these platforms aren’t useful; rather, I think the opportunity that students have to use them is inadequate [K4]. I feel this is because of the severely limited temporal environment of the current teaching format of short, intensive courses [K6]. This is compounded by occasional technological issues, such as a student’s computer malfunctioning. I find issues like these frustrating because they result in a poor student experience; students typically cannot complete the tasks and rarely have time to catch up. As outlined in Blog 2, I intend to improve the course format as part of the School’s upcoming revalidation [A5, K6, V3].
Reflective Blog 4: Assessment for Learning – methodologies for assessment and feedback
As BI1002 Assessment Lead I coordinate multiple assessments, including a summative, Skills-Based Assignment (SBA). SBAs follow the principle of “Assessment for Learning” –that assessments are integral to the learning experience (Brown, 2005) [K1]. SBAs contribute to constructive alignment developing knowledge and skills linked to learning outcomes. I think that the BI1002 SBA meets many of these principles; it reinforces the curriculum, consolidates knowledge and understanding, encourages inquiry, develops professional skills, and enables students to evaluate progress. However, one area I feel this assessment fails is in accurately assessing students’ ability; I think the assessment penalises too harshly for spelling mistakes [K2]. For example, students must identify anatomical structures in the human thorax.
The correct answer for A is “right auricle” (Fig. 1); misspelling it as “right aurical” is marked incorrect2. Accuracy in science is crucial, and it’s a key professional attitude and skill that employers look for [V4]. However, this question assesses the students’ ability to identify anatomical structures. Therefore, I feel “right aurical” should be correct; if this was an oral assessment, spelling mistakes couldn’t occur. I fear that marking too harshly for spelling mistakes can lead to students obtaining a score that does not reflect their ability [K6]. This can create disappointment, and risks demotivation or disengagement [K3, V2]. This approach also raises inclusivity concerns. Currently, students with “reasonable adjustments” for spelling are not penalised [K2, V1, V2]. However, research shows some students do not declare disabilities (Jacklin, 2011), 8% have an unseen disability (Cameron et al., 2019), and many are likely unaware they’re disabled; research suggests that 10% of people are dyslexic (Bishop, 2015), yet only 12 of 460 students (2.6%) requested reasonable adjustments. To address this, I intend to refine the marking system to assess students’ abilities more appropriately and equitably [A3, A5, V1, V2]. This will involve exploring more inclusive assessment options, such oral assessment via audio recordings [A1, K4].
I also mark Final Year Project (FYP) oral presentations. Prior to this summative assessment, students cannot conduct a formative presentation and supervisors cannot provide advice. I feel this is ludicrous, not least because it lacks a basis in pedagogic theory; students find receiving no feedback to be unhelpful (Blair et al., 2013) [K3, K6]. Ironically, I must provide feedback on the summative presentation, but I wonder how likely students are to engage with it since this is the final oral presentation of their degree. If students aren’t engaging with feedback, it isn’t feedback [K5]! If this wasn’t bad enough, the module does not facilitate opportunities for peer feedback. Peer feedback is arguably one of the most powerful learning activities for students (Phillips, 2016) because it allows them to critically appraise someone else’s work and reflect on their own in comparison (Liu & Carless, 2006) [K2, K3]. This year I arranged a session for students to practice their presentations together [A1, A2, V2]. I was present, but in the interest of fair practice I didn’t provide feedback [K6]. Instead, I encouraged students to generate feedback collaboratively, and facilitated this by providing students with the mark scheme to assess one another [A4, K4, V2]. It was clear that this opportunity helped students calm their nerves ahead of the summative assessment [V1].
I raised the importance of peer feedback in a recent Board of Studies meeting [A5, K6], although was met with stern, ignorant resistance; one colleague said providing a space for students to generate peer feedback is analogous to “the blind leading the blind”. Notwithstanding the disablist rhetoric, this highlights the need to educate colleagues in appropriate pedagogic techniques [K6]. I intend to design a formative exercise where students can view and appraise previous presentations, generating their own marks and feedback to compare this with staff-generated feedback [A1, A5, K2, V2]. This would introduce minimal workload for staff [K6, V4]. I hope that successful implementation of this will also help to educate colleagues.
The correct answer for A is “right auricle” (Fig. 1); misspelling it as “right aurical” is marked incorrect2. Accuracy in science is crucial, and it’s a key professional attitude and skill that employers look for [V4]. However, this question assesses the students’ ability to identify anatomical structures. Therefore, I feel “right aurical” should be correct; if this was an oral assessment, spelling mistakes couldn’t occur. I fear that marking too harshly for spelling mistakes can lead to students obtaining a score that does not reflect their ability [K6]. This can create disappointment, and risks demotivation or disengagement [K3, V2]. This approach also raises inclusivity concerns. Currently, students with “reasonable adjustments” for spelling are not penalised [K2, V1, V2]. However, research shows some students do not declare disabilities (Jacklin, 2011), 8% have an unseen disability (Cameron et al., 2019), and many are likely unaware they’re disabled; research suggests that 10% of people are dyslexic (Bishop, 2015), yet only 12 of 460 students (2.6%) requested reasonable adjustments. To address this, I intend to refine the marking system to assess students’ abilities more appropriately and equitably [A3, A5, V1, V2]. This will involve exploring more inclusive assessment options, such oral assessment via audio recordings [A1, K4].
I also mark Final Year Project (FYP) oral presentations. Prior to this summative assessment, students cannot conduct a formative presentation and supervisors cannot provide advice. I feel this is ludicrous, not least because it lacks a basis in pedagogic theory; students find receiving no feedback to be unhelpful (Blair et al., 2013) [K3, K6]. Ironically, I must provide feedback on the summative presentation, but I wonder how likely students are to engage with it since this is the final oral presentation of their degree. If students aren’t engaging with feedback, it isn’t feedback [K5]! If this wasn’t bad enough, the module does not facilitate opportunities for peer feedback. Peer feedback is arguably one of the most powerful learning activities for students (Phillips, 2016) because it allows them to critically appraise someone else’s work and reflect on their own in comparison (Liu & Carless, 2006) [K2, K3]. This year I arranged a session for students to practice their presentations together [A1, A2, V2]. I was present, but in the interest of fair practice I didn’t provide feedback [K6]. Instead, I encouraged students to generate feedback collaboratively, and facilitated this by providing students with the mark scheme to assess one another [A4, K4, V2]. It was clear that this opportunity helped students calm their nerves ahead of the summative assessment [V1].
I raised the importance of peer feedback in a recent Board of Studies meeting [A5, K6], although was met with stern, ignorant resistance; one colleague said providing a space for students to generate peer feedback is analogous to “the blind leading the blind”. Notwithstanding the disablist rhetoric, this highlights the need to educate colleagues in appropriate pedagogic techniques [K6]. I intend to design a formative exercise where students can view and appraise previous presentations, generating their own marks and feedback to compare this with staff-generated feedback [A1, A5, K2, V2]. This would introduce minimal workload for staff [K6, V4]. I hope that successful implementation of this will also help to educate colleagues.
Reflective Blog 5: Student support
Student transitions occur throughout their journey (Gale & Parker, 2014) but the most critical transition for student development is their first year at university (Yorke & Longden, 2008; Krause & Coates, 2008) [K3]. This transition presents academic and pastoral challenges such as homesickness (Thurber & Walton, 2012), forced social interaction (Hotez et al., 2018), time and money management (Macan et al., 1990; Montalto et al., 2019), unexpectedly poor academic attainment (Blondal & Adalbjarnardottir, 2012), and culture shock (Furnham, 2004). Supporting students academically and pastorally are not mutually exclusive [K4] and I strive to do both. I think a key challenge to supporting students is in breaking down barriers between students and staff; I often feel that students see lecturers only as academics and forget that “we are real people too”. This can stifle the potential for positive, meaningful interactions and thus connections between teachers and learners. I support the notion that students should be partners in learning (Harrington et al., 2014); building connection is essential to achieve this (Fraser & Walberg, 2005) [K3].
I have helped build connections with students by helping to create a “Getting to know your lecturer” podcast series which sees Y2 and Y3 undergraduates interview staff members that Y1 students will meet [A2, A5, K4]. The podcasts aim to build a sense community, and help new students find out who their new teachers are. I feel this is crucial because students meet many staff members in Y1. Approximately half of the students have engaged with the podcasts and the Module Lead has received substantial positive feedback. This makes me feel proud and optimistic that we are moving in the right direction. In fact, at a recent staff-student panel, the Y2 student rep requested the podcast be released to Y2 students. Thus, we plan to roll out the activity across other modules and years [A5, V1, V2].
I have also supported the student community by presenting at a Bioscience Society seminar designed to enrich the student experience [A2]. I feel it is important to engage with societies because it helps both myself and students feel less like it’s “us and them”, and more like we’re partners, developing a sense of belonging for both [V1]. My presentation focused on my journey from loathing to loving statistics. I shared personal anecdotes about my statistics anxiety and inability, including sensitive accounts such as failing to be recruited for a PhD project because of it. Several students contacted me afterwards to say they found my story interesting, not least the revelation that my background is in ecology, not statistics! I feel that the talk helped students to see that though they may be struggling, there is always hope that this will change, just as it did for me [A4, V2]. Moreover, being vulnerable allowed students to become more familiar with me as an individual, contributing to a sense of community [A4].
I feel that my compassionate and personable approach, combined with academic expertise, enables me to support students both academically and pastorally. However, I recently supervised a Final Year Project student whom until the day before their deadline, had interacted with me positively and raised no cause for concern. Much to my surprise, the student failed to submit their dissertation. In hindsight, though the student had engaged with formative assignments and communicated well, I had not seen their work for several weeks. I felt like a complete failure, thinking that I had not provided enough support and had missed signs that the student hadn’t made as much progress as they let on. That said, I don’t condone “student blaming” (Finch & Taylor, 2013) and on reflection I feel that I could have done more. In future, I will strive to be better aware of students’ circumstances although I understand that students are not obliged disclose any issues [V1]. I also plan to attend the university's "Introduction to Personal Tutoring" course and familiarise myself with the School of Biosciences' Personal Tutor Toolkit to prepare for future tutoring responsibilities, which I am looking forward to [A5, K4, K6, V3].
I have helped build connections with students by helping to create a “Getting to know your lecturer” podcast series which sees Y2 and Y3 undergraduates interview staff members that Y1 students will meet [A2, A5, K4]. The podcasts aim to build a sense community, and help new students find out who their new teachers are. I feel this is crucial because students meet many staff members in Y1. Approximately half of the students have engaged with the podcasts and the Module Lead has received substantial positive feedback. This makes me feel proud and optimistic that we are moving in the right direction. In fact, at a recent staff-student panel, the Y2 student rep requested the podcast be released to Y2 students. Thus, we plan to roll out the activity across other modules and years [A5, V1, V2].
I have also supported the student community by presenting at a Bioscience Society seminar designed to enrich the student experience [A2]. I feel it is important to engage with societies because it helps both myself and students feel less like it’s “us and them”, and more like we’re partners, developing a sense of belonging for both [V1]. My presentation focused on my journey from loathing to loving statistics. I shared personal anecdotes about my statistics anxiety and inability, including sensitive accounts such as failing to be recruited for a PhD project because of it. Several students contacted me afterwards to say they found my story interesting, not least the revelation that my background is in ecology, not statistics! I feel that the talk helped students to see that though they may be struggling, there is always hope that this will change, just as it did for me [A4, V2]. Moreover, being vulnerable allowed students to become more familiar with me as an individual, contributing to a sense of community [A4].
I feel that my compassionate and personable approach, combined with academic expertise, enables me to support students both academically and pastorally. However, I recently supervised a Final Year Project student whom until the day before their deadline, had interacted with me positively and raised no cause for concern. Much to my surprise, the student failed to submit their dissertation. In hindsight, though the student had engaged with formative assignments and communicated well, I had not seen their work for several weeks. I felt like a complete failure, thinking that I had not provided enough support and had missed signs that the student hadn’t made as much progress as they let on. That said, I don’t condone “student blaming” (Finch & Taylor, 2013) and on reflection I feel that I could have done more. In future, I will strive to be better aware of students’ circumstances although I understand that students are not obliged disclose any issues [V1]. I also plan to attend the university's "Introduction to Personal Tutoring" course and familiarise myself with the School of Biosciences' Personal Tutor Toolkit to prepare for future tutoring responsibilities, which I am looking forward to [A5, K4, K6, V3].
Reflective Blog 6: Professional Planning
I evaluate my statistics courses through student feedback obtained via Microsoft Forms surveys, polls on Slack, and individual discussions [A5, K4, K5]. In my experience, providing multiple feedback options promotes participation [V2].
This year, I received feedback from 19 students following the Y2 statistics course, including free-text comments with suggestions for improvement. The key themes were:
In response to (iv), I have started creating an “RStudio accessibility” document and short video tutorials (≤ 30 seconds) explaining how to adjust RStudio settings [A4, A5, K4, V1, V3]. These videos will form part of a larger online resource bank that I will create this summer, which will have screencast tutorials on each statistical test taught and how to interpret their outputs [A1]; such resources have been shown to enhance student learning of statistics (Lloyd & Robertson, 2012) [V3]. These resources will be short (≤ 5 mins) to increase engagement [V2] and I intend to use them resources in a flipped-classroom format [A1].
That students asked for a glossary of R functions is surprising, because glossaries already exist as part of the Statspack [K1]. Clearly, I have not been signposting students to the Statspack well enough. In the next round of teaching, I will stress this at the beginning of the course [A2] and check students’ engagement midway through using Mentimeter [A3, K4,
K5, V2]. Moreover, currently there are separate glossaries for each teaching session (Fig. 3) which I will combine into one master glossary for students to use throughout the course [A4, K4]. I feel that doing this will make it easier for students to keep track of the functions they have used by reducing the need to switch between different documents [V2].
Incorporating other datasets into the course (i.e., suggestion iii) will be straightforward, and I plan to do this over summer when I revise the teaching materials. Providing datasets relevant to each audience’s disciplines (Biology, Biochemistry, Biomedical Sciences, Neuroscience, and Zoology) will facilitate authentic learning [K3, V2], which is crucial for understanding statistics (Lau & Tasir, 2018) [K6, V3].
Last year, feedback for my PhD-level course included a request to teach Bayesian methods. Thus, I undertook relevant professional development this year, attending a 3-day training course on Bayesian statistics and reading Inchausti (2022) [A5]. Time constraints meant that I couldn’t incorporate this new expertise directly into this year’s teaching materials, however I did share details of my training and relevant resources with students [A2], with plans to incorporate Bayesian methods into next year’s course [V3]. I will compare Bayesian methods with Frequentist statistics (currently taught) and highlight the latter’s potential limitations [A1, K1]. Additionally, 64% of this year’s students reported that they would have preferred the entire course to be delivered in person rather than half online [K5]. I will discuss this option with the Director of the PhD programme and consider accessibility and inclusivity needs for the venue and wider learning environment [K6, V3]. Finally, last year’s summative assessment was marked manually on Turnitin. This year, I will implement the assessment on Möbius which provides automatic marking capabilities [K4]. This will not only reduce staff workload [V4] but will also provide a better learning experience for students, as their formative assessments are already hosted on this platform [A4, K2, K3, K6, V2].
This year, I received feedback from 19 students following the Y2 statistics course, including free-text comments with suggestions for improvement. The key themes were:
- “In the lectures, focus more on explaining R code and interpreting outputs, rather than statistical theory”.
- “Provide a glossary of main R functions used during the week”.
- “Use a broader range of datasets beyond ecology-related ones”.
- “Include information on configuring RStudio for accessibility”.
In response to (iv), I have started creating an “RStudio accessibility” document and short video tutorials (≤ 30 seconds) explaining how to adjust RStudio settings [A4, A5, K4, V1, V3]. These videos will form part of a larger online resource bank that I will create this summer, which will have screencast tutorials on each statistical test taught and how to interpret their outputs [A1]; such resources have been shown to enhance student learning of statistics (Lloyd & Robertson, 2012) [V3]. These resources will be short (≤ 5 mins) to increase engagement [V2] and I intend to use them resources in a flipped-classroom format [A1].
That students asked for a glossary of R functions is surprising, because glossaries already exist as part of the Statspack [K1]. Clearly, I have not been signposting students to the Statspack well enough. In the next round of teaching, I will stress this at the beginning of the course [A2] and check students’ engagement midway through using Mentimeter [A3, K4,
K5, V2]. Moreover, currently there are separate glossaries for each teaching session (Fig. 3) which I will combine into one master glossary for students to use throughout the course [A4, K4]. I feel that doing this will make it easier for students to keep track of the functions they have used by reducing the need to switch between different documents [V2].
Incorporating other datasets into the course (i.e., suggestion iii) will be straightforward, and I plan to do this over summer when I revise the teaching materials. Providing datasets relevant to each audience’s disciplines (Biology, Biochemistry, Biomedical Sciences, Neuroscience, and Zoology) will facilitate authentic learning [K3, V2], which is crucial for understanding statistics (Lau & Tasir, 2018) [K6, V3].
Last year, feedback for my PhD-level course included a request to teach Bayesian methods. Thus, I undertook relevant professional development this year, attending a 3-day training course on Bayesian statistics and reading Inchausti (2022) [A5]. Time constraints meant that I couldn’t incorporate this new expertise directly into this year’s teaching materials, however I did share details of my training and relevant resources with students [A2], with plans to incorporate Bayesian methods into next year’s course [V3]. I will compare Bayesian methods with Frequentist statistics (currently taught) and highlight the latter’s potential limitations [A1, K1]. Additionally, 64% of this year’s students reported that they would have preferred the entire course to be delivered in person rather than half online [K5]. I will discuss this option with the Director of the PhD programme and consider accessibility and inclusivity needs for the venue and wider learning environment [K6, V3]. Finally, last year’s summative assessment was marked manually on Turnitin. This year, I will implement the assessment on Möbius which provides automatic marking capabilities [K4]. This will not only reduce staff workload [V4] but will also provide a better learning experience for students, as their formative assessments are already hosted on this platform [A4, K2, K3, K6, V2].
Portfolio Narrative
My teaching experience prior to joining Cardiff University was extensive, but undertaking the CUEFP revealed that I hadn’t fully considered my approach. Reflecting on my practice has given me a profound understanding of this and the professional values that underpin it, and has helped develop my teaching philosophy and grow as an educator. This process has been mostly enjoyable, occasionally uncomfortable, and at times cathartic; importantly it has helped me to understand the decisions I had made in my teaching, justification(s) I had used, and feelings I had experienced. Familiarising myself with pedagogic literature and the UKPSF has been very rewarding, highlighting where my approaches align with best practice or not; I see clear areas of strength, and room for improvement. I now understand that teaching and professional development are innately linked [K1], and I now self-evaluate my practice regularly [A5, K5].
I feel that since starting the CUEFP, my teaching philosophy has evolved. Initially, it was grounded in ‘folk pedagogy’ [K1]; based heavily on my experience of learning statistics and the learner-focused approach used by my supervisor to remedy that. While this approach worked for me, I now see that it can be exclusive to some learners [V1], and that I hadn’t properly considered alternative approaches. My experiences this year – especially where students weren’t receptive to a learner-focused approach – have revealed that sticking to what I know isn’t always effective, and I am now confident to try alternatives [K2]. I feel my teaching philosophy has moved towards the middle between teacher-focused and learner-focused, with a slight leaning towards the right (Samuelowicz and Bain 2001) [K1]. This shift is largely due to my greater understanding of the ways that students learn (Donovan & Bransford, 2005) [K3, V1]. I am now revising my approach to provide a more inclusive environment for all. For example, I am accommodating self-regulated learning (Boekaerts, 1999) by delivering resources in a teacher-focused manner and allowing students to interact asynchronously [A2, K4], rather than having to engage directly.
When I first learnt about Universal Design (Cast, 2021; Thomas, 2021), I felt overwhelmed; it was intimidating to reflect on if I had designed my teaching to accommodate the vast diversity of needs found within a student body. Clearly I hadn’t given this enough consideration – something I’m now committed to improving [A5]. The concept of inclusivity by design is important for me because I teach and assess very large class sizes, which will inevitably include a variety of learning types and needs [K1, K3]. I had considered colour-blindness extensively due to my own partial red-green deficiency, but my teaching lacked consideration for other diversity. Having reflected, I'm relieved that my teaching is quite inclusive by design. Nevertheless, my professional values are undermined by a lack of consideration regarding inclusivity, and I was disappointed in myself in this regard. I have since sought to place inclusivity at the heart of my teaching and have identified clear areas where inclusivity can be improved to achieve equity for students [K6, V1, V2]. For example, ensuring that assignments are marked appropriately and offering multiple modes of assessment [A1, A3, K2].
The concept of constructive alignment and the principle of Assessment for Learning have been key sources of inspiration in considering how to assess students effectively [K1]. I am now refining Y1 SBAs and creating new assessments with these in mind [A1, V3]. Recent revelations about feedback, particularly the notion that feedback isn’t feedback unless it’s used [K2, K3], have been inspiring. Naomi Winstone’s recent seminar highlighted that much of my feedback is transmissional and transactional – I aim in future to generate feedback collaboratively with students rather than deliver it to them [A3, A5]. This, I hope, will foster an environment where feedback is used. I’m inspired to use audio and Mentimeter more frequently for assessment and feedback [K5]. Moreover, I’m aware of the need for more formative assessment and teaching feedback literacy [A4, K4]. In fact, my subject area lends itself to this – statistics is repeatable and revising the basics is always useful, hence more formative assessment can be effective here [K1, K2]. Formative assessments promote experiential learning which I’m pleased to see is particularly effective for teaching statistics (Hakeem, 2001). I plan to continue to facilitate this and use continuous assessment to adapt teaching regularly for maximum effectiveness [V3].
The format of statistics courses came up often in my reflection. In particular, the fact that statistics is taught in short, intensive blocks – something that promotes surface learning (Diamond, 2011) [K3] – and that the content I am expected to teach is too vast given the time that I have. This is a programme-level issue that has been beyond my control, however I have used Board of Studies meetings as an outlet to voice my concerns [K6]. Revalidation offers an opportunity to tackle this, and I am working with colleagues to restructure the teaching; building on work I have already done to embed statistics into Y2 practicals and Y1 SBAs [A2, A4]. Revalidation also offers a chance to consider the implications of assurance and enhancement in a wider context [K6, V4]. To support this, I will build on my role as Assessment Lead and take on more management responsibilities within the School [A5]. To prepare for this, I am attending the Leading Learning and Teaching Programme which complements what I’ve learned by encouraging me to consider how my compassionate leadership style impacts my teaching [K2, K6, V3].
The last key theme was my passion for developing a supportive learning environment which both provides excellent teaching and fosters a sense of community and positive student experience. I have relished opportunities to break down barriers and promote participation, such as through delivering talks for the Bioscience Society and developing a podcast series [A2, A4, A5, V2]. I am now in discussion with the Student Experience (Community) Lead to support the teaching of statistics more holistically by contributing further to research talks on statistics anxiety and sharing my personal journey [A2], which have resonated well with students previously [A3]. Further, statistics anxiety will be the key theme to tackle in my scholarship and research work; this will be the golden thread linking my efforts towards future awards, such as National Teaching Fellow [A5].
Overall, this journey fills me with pride for what I have achieved this year, in particular working with students to inspire and motivate them to reach their potential, exemplified by this final feedback anecdote: “William is by far the best statistics teacher I have ever had! He was very clear in his points and took the time to address individual questions one-on-one. His love for teaching really shone through and I think this made a huge difference to my understanding and enjoyment of the course.” I am excited for the future; most keenly for the many experiences I will create together with students, but also to further refine my teaching practice and fulfil my next ambition of becoming SFHEA [A5].
I feel that since starting the CUEFP, my teaching philosophy has evolved. Initially, it was grounded in ‘folk pedagogy’ [K1]; based heavily on my experience of learning statistics and the learner-focused approach used by my supervisor to remedy that. While this approach worked for me, I now see that it can be exclusive to some learners [V1], and that I hadn’t properly considered alternative approaches. My experiences this year – especially where students weren’t receptive to a learner-focused approach – have revealed that sticking to what I know isn’t always effective, and I am now confident to try alternatives [K2]. I feel my teaching philosophy has moved towards the middle between teacher-focused and learner-focused, with a slight leaning towards the right (Samuelowicz and Bain 2001) [K1]. This shift is largely due to my greater understanding of the ways that students learn (Donovan & Bransford, 2005) [K3, V1]. I am now revising my approach to provide a more inclusive environment for all. For example, I am accommodating self-regulated learning (Boekaerts, 1999) by delivering resources in a teacher-focused manner and allowing students to interact asynchronously [A2, K4], rather than having to engage directly.
When I first learnt about Universal Design (Cast, 2021; Thomas, 2021), I felt overwhelmed; it was intimidating to reflect on if I had designed my teaching to accommodate the vast diversity of needs found within a student body. Clearly I hadn’t given this enough consideration – something I’m now committed to improving [A5]. The concept of inclusivity by design is important for me because I teach and assess very large class sizes, which will inevitably include a variety of learning types and needs [K1, K3]. I had considered colour-blindness extensively due to my own partial red-green deficiency, but my teaching lacked consideration for other diversity. Having reflected, I'm relieved that my teaching is quite inclusive by design. Nevertheless, my professional values are undermined by a lack of consideration regarding inclusivity, and I was disappointed in myself in this regard. I have since sought to place inclusivity at the heart of my teaching and have identified clear areas where inclusivity can be improved to achieve equity for students [K6, V1, V2]. For example, ensuring that assignments are marked appropriately and offering multiple modes of assessment [A1, A3, K2].
The concept of constructive alignment and the principle of Assessment for Learning have been key sources of inspiration in considering how to assess students effectively [K1]. I am now refining Y1 SBAs and creating new assessments with these in mind [A1, V3]. Recent revelations about feedback, particularly the notion that feedback isn’t feedback unless it’s used [K2, K3], have been inspiring. Naomi Winstone’s recent seminar highlighted that much of my feedback is transmissional and transactional – I aim in future to generate feedback collaboratively with students rather than deliver it to them [A3, A5]. This, I hope, will foster an environment where feedback is used. I’m inspired to use audio and Mentimeter more frequently for assessment and feedback [K5]. Moreover, I’m aware of the need for more formative assessment and teaching feedback literacy [A4, K4]. In fact, my subject area lends itself to this – statistics is repeatable and revising the basics is always useful, hence more formative assessment can be effective here [K1, K2]. Formative assessments promote experiential learning which I’m pleased to see is particularly effective for teaching statistics (Hakeem, 2001). I plan to continue to facilitate this and use continuous assessment to adapt teaching regularly for maximum effectiveness [V3].
The format of statistics courses came up often in my reflection. In particular, the fact that statistics is taught in short, intensive blocks – something that promotes surface learning (Diamond, 2011) [K3] – and that the content I am expected to teach is too vast given the time that I have. This is a programme-level issue that has been beyond my control, however I have used Board of Studies meetings as an outlet to voice my concerns [K6]. Revalidation offers an opportunity to tackle this, and I am working with colleagues to restructure the teaching; building on work I have already done to embed statistics into Y2 practicals and Y1 SBAs [A2, A4]. Revalidation also offers a chance to consider the implications of assurance and enhancement in a wider context [K6, V4]. To support this, I will build on my role as Assessment Lead and take on more management responsibilities within the School [A5]. To prepare for this, I am attending the Leading Learning and Teaching Programme which complements what I’ve learned by encouraging me to consider how my compassionate leadership style impacts my teaching [K2, K6, V3].
The last key theme was my passion for developing a supportive learning environment which both provides excellent teaching and fosters a sense of community and positive student experience. I have relished opportunities to break down barriers and promote participation, such as through delivering talks for the Bioscience Society and developing a podcast series [A2, A4, A5, V2]. I am now in discussion with the Student Experience (Community) Lead to support the teaching of statistics more holistically by contributing further to research talks on statistics anxiety and sharing my personal journey [A2], which have resonated well with students previously [A3]. Further, statistics anxiety will be the key theme to tackle in my scholarship and research work; this will be the golden thread linking my efforts towards future awards, such as National Teaching Fellow [A5].
Overall, this journey fills me with pride for what I have achieved this year, in particular working with students to inspire and motivate them to reach their potential, exemplified by this final feedback anecdote: “William is by far the best statistics teacher I have ever had! He was very clear in his points and took the time to address individual questions one-on-one. His love for teaching really shone through and I think this made a huge difference to my understanding and enjoyment of the course.” I am excited for the future; most keenly for the many experiences I will create together with students, but also to further refine my teaching practice and fulfil my next ambition of becoming SFHEA [A5].
References
- Adawiyah, R. A., Diani, I., & Azwandi, A. (2021). Illocutionary Acts Used in the Dead Poets Society Movie. Silampari Bisa: Jurnal Penelitian Pendidikan Bahasa Indonesia, Daerah, dan Asing, 4(1), 122-132.
- Altemueller, L., & Lindquist, C. (2017). Flipped classroom instruction for inclusive learning. British Journal of Special Education, 44(3), 341-358.
- Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher education, 32(3), 347-364
- Bishop, D. V. (2015). The interface between genetics and psychology: lessons from developmental dyslexia. Proceedings of the Royal Society B: Biological Sciences, 282(1806), 20143139.
- Blair, A., Curtis, S., Goodwin, M., & Shields, S. (2013). What feedback do students want?. Politics, 33(1), 66-79.
- Blondal, K. S., & Adalbjarnardottir, S. (2012). Student disengagement in relation to expected and unexpected educational pathways. Scandinavian Journal of Educational Research, 56(1), 85-100.
- Bloom, B. S., Englehart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: Handbook I. Cognitive domain. New York: David McKay.
- Boekaerts, M. (1999). Self-regulated learning: Where we are today. International journal of educational research, 31(6), 445-457.
- Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn (Vol. 11). Washington, DC: National academy press.
- Brown, S. (2005). Assessment for learning. Learning and teaching in higher education, (1), 81-89.
- Bruffee, K. A. (1993). Collaborative learning.
- Bruner, J. (1978). The role of dialogue in language acquisition. The child’s conception of language, 2.
- Bruner, J. (1999). Folk pedagogies. Learners and pedagogy, 1(1), 4-20.
- Cameron, H., Coleman, B., Hervey, T., Rahman, S., & Rostant, P. (2019). Equality Law Obligations in Higher Education: reasonable adjustments under the Equality Act 2010 in assessment of students with unseen disabilities. Legal Studies, 39(2), 204-229.
- Carlson, K. A., & Winquist, J. R. (2011). Evaluating an active learning approach to teaching introductory statistics: A classroom workbook approach. Journal of Statistics Education, 19(1).
- Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences, 116(39), 19251-19257.
- Diamond, R. V. (2011). Analysis of assessment data from statistics courses: Grade distributions, surface learning and threshold concepts. Surface Learning and Threshold Concepts (July 20, 2011).
- Donovan, S., & Bransford, J. (2005). How students learn. Washington, DC: National Academies Press.
- Dweck, C. S. (2006). Mindset: The new psychology of success. Random house.
- Felder, R. M., & Brent, R. (2009). Active learning: An introduction. ASQ higher education brief, 2(4), 1-5.
- Finch, J., & Taylor, I. (2013). Failure to fail? Practice educators' emotional experiences of assessing failing social work students. Social Work Education, 32(2), 244-258.
- Fox, D. (1983). Personal theories of teaching. Studies in higher education, 8(2), 151-163.
- Fraser, B. J., & Walberg, H. J. (2005). Research on teacher–student relationships and learning environments: Context, retrospect and prospect. International Journal of educational research, 43(1-2), 103-109.
- Friedmann, E., & Son, H. (2009). The human–companion animal bond: how humans benefit. Veterinary Clinics of North America: Small Animal Practice, 39(2), 293-326.
- Furnham, A. (2004). Education and culture shock. Psychologist, 17(1), 16.
- Gale, T., & Parker, S. (2014). Navigating change: a typology of student transition in higher education. Studies in higher education, 39(5), 734-753.
- Garfield, J., & Ben‐Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International statistical review, 75(3), 372-396.
- Gesler, D. (In prep). It’s Only Rock ‘N’ Roll But I Like It: How Songs Can Help Teach Statistics. Murray State University, Murray, Kentucky 42071.
- Hakeem, S. A. (2001). Effect of experiential learning in business statistics. Journal of Education for Business, 77(2), 95-98.
- Harrington, K., Flint, A., & Healey, M. (2014). Engagement through partnership: Students as partners in learning and teaching in higher education.
- Hotez, E., Shane-Simpson, C., Obeid, R., DeNigris, D., Siller, M., Costikas, C., Pickens, J., Massa, A., Giannola, M., D’Onofrio, J., & Gillespie-Lynch, K. (2018).
- Designing a summer transition program for incoming and current college students on the autism spectrum: A participatory approach. Frontiers in Psychology, 9, 46.
- Inchausti, P. (2023). Statistical Modeling With R: a dual frequentist and Bayesian approach for life scientists. Oxford University Press.
- Jacklin, A. (2011). To be or not to be ‘a disabled student’ in higher education: the case of a postgraduate ‘non‐declaring’(disabled) student. Journal of research in special educational needs, 11(2), 99-106.
- Kember, D., & Kwan, K. P. (2000). Lecturers' approaches to teaching and their relationship to conceptions of good teaching. Instructional science, 28, 469-490.
- Knoblich, G., & Oellinger, M. (2006). The eureka moment. Scientific American Mind, 17(5), 38-43.
- Krause, K. L., & Coates, H. (2008). Students’ engagement in first‐year university. Assessment & Evaluation in Higher Education, 33(5), 493-505.
- Langan, D. (2022) Teaching Statistics in Context: Effects of Statistics History on Student Learning. Burwalls 2022 Annual Meeting for Teachers of Statistics in Medicine and Allied Health Sciences, 11th July 2022.
- Lau, U. H., & Tasir, Z. (2018). The design and development of online authentic learning environment for knowledge construction in learning inferential statistics. The Journal of Social Sciences Research, 71-79.
- Linder, K. E. (2017). Fundamentals of hybrid teaching and learning. New directions for teaching and learning, 2017(149), 11-18.
- Lindsay, G. (2003). Inclusive education: a critical perspective. British journal of special education, 30(1), 3-12.
- Liu, N. F., & Carless, D. (2006). Peer feedback: the learning element of peer assessment. Teaching in Higher education, 11(3), 279-290.
- Lloyd, S. A., & Robertson, C. L. (2012). Screencast tutorials enhance student learning of statistics. Teaching of Psychology, 39(1), 67-71.
- Macan, T. H., Shahani, C., Dipboye, R. L., & Phillips, A. P. (1990). College students' time management: Correlations with academic performance and stress. Journal of educational psychology, 82(4), 760.
- Macher, D., Papousek, I., Ruggeri, K., & Paechter, M. (2015). Statistics anxiety and performance: blessings in disguise. Frontiers in psychology, 6, 1116.
- Malcolm, J., Hodkinson, P., & Colley, H. (2003). The interrelationships between informal and formal learning. Journal of workplace learning, 15(7/8), 313-318.
- Maslow, A. H. (1943). A theory of human motivation. Psychological review, 50(4), 370.
- Medeiros, R., Vafidis, J. O., Smith, J. A., & Thomas, R. J. (2023). Teaching data analysis to life scientists using “R” statistical software: challenges, opportunities,
- and effective methods. In Farnell & Medeiros (1st ed.) Teaching Biostatistics in Medicine and Allied Health Sciences.
- Miller, J. P. (2005). Educating for wisdom and compassion: Creating conditions for timeless learning. Corwin Press.
- Mizokami, S. (2018). Deep active learning from the perspective of active learning theory. Deep active learning: Toward greater depth in university education, 79-91.
- Monahan, N. (2013). Keeping introverts in mind in your active learning classroom. Faculty focus.
- Montalto, C. P., Phillips, E. L., McDaniel, A., & Baker, A. R. (2019). College student financial wellness: Student loans and beyond. Journal of Family and Economic Issues, 40(1), 3-21.
- Morris, C. (2022). Practical Considerations in Universal Design. Table presented in Cardiff University Education Fellowship
- Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics Anxiety: Nature, etiology, antecedents, effects, and treatments – a comprehensive review of the literature. Teaching in higher education, 8(2), 195-209.
- Paul, R., & Elder, L. (2019). The thinker's guide to Socratic questioning. Rowman & Littlefield.
- Peters, S. J. (2004). Inclusive education: An EFA strategy for all children. Washington, DC: World Bank, Human Development Network.
- Phillips, F. (2016). The power of giving feedback: Outcomes from implementing an online peer assessment system. Issues in accounting education, 31(1), 1-15.
- Samuelowicz, K. and Bain, J.D. (1992). Conceptions of teaching held by academic teachers. Higher Education, 24, 93–111
- Samuelowicz, K. and Bain, J.D. (2001). Revisiting academics' beliefs about teaching and learning. Higher education, 41, 299-325.
- Schacht, S., & Stewart, B. J. (1990). What's funny about statistics? A technique for reducing student anxiety. Teaching Sociology, 18(1), 52-56.
- Stalder, D. R., & Olson, E. A. (2011). T for two: Using mnemonics to teach statistics. Teaching of Psychology, 38(4), 247-250.
- Thurber, C. A., & Walton, E. A. (2012). Homesickness and adjustment in university students. Journal of American college health, 60(5), 415-419.
- Trassi, A.P., Leonard, S.J., Rodrigues, L.D., Rodas, J.A. and Santos, F.H., 2022. Mediating factors of statistics anxiety in university students: a systematic review and meta‐analysis. Annals of the New York Academy of Sciences, 1512(1), pp.76-97.
- Weltman, D., & Whiteside, M. (2010). Comparing the effectiveness of traditional and active learning methods in business statistics: Convergence to the mean. Journal of Statistics Education, 18(1).
- Wertsch, J. V. (1984). The zone of proximal development: Some conceptual issues. New Directions for Child and Adolescent Development, 1984(23), 7-18.
- Wiggins, G., Wiggins, G. P., & McTighe, J. (2005). Understanding by design. Ascd.
- Wilson, V. A. (1999). Reducing Statistics Anxiety: a ranking of sixteen specific strategies. Paper presented at the annual meeting of the Mid-South Educational Research Association, Point Clear, AL, November.
- Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In, DJ Hacker, J. Dunlosky, & AC Graesser. Metacognition in educational theory and practice, 277-304.
- Winstone, N. E. (2022). Characterising feedback cultures in higher education: an analysis of strategy documents from 134 UK universities. Higher Education, 84(5), 1107-1125.
- Yorke, M., & Longden, B. (2008). The first-year experience in higher education in the UK.