Experiential learning in physical geography using arduino low-cost environmental sensors

ABSTRACT

Transmission teaching which centres around traditional lecturing discounts the variety of different learners and individual aptitudes. Physical Geography as a discipline has historically provided a range of teaching methods beyond lecturing which embrace field and laboratory activities, frequently adapting new research technologies to further student learning. While technological trends are increasing the demand for Geography graduates with GIS, modelling, or programming skills, Geography lecturers should remain open to using other technological advances as teaching tools. Using an example of low-cost environmental sensors, this paper demonstrates how technologically-focused exercises can effectively solidify a range of geographical skills through experiential learning. Using Kolb’s Experiential Learning Model to identify the key learning processes, we compare low-cost environmental sensor training to the UK’s current Quality Assurance Agency for Higher Education’s (QAA) Geography benchmarks. We also consider the practical applications of this technology as a learning tool for Physical Geography. In comparison with a student evaluation, this paper provides an initial basis to support additional qualitative investigations into the learning outcomes of independent, technology-based learning activities.

KEYWORDS:

• Arduino
• environmental monitoring
• experiential learning
• sensor networks
• learning benchmarks

Introduction

Higher education (HE) institutions have evolved due to a range global and national socio-economic and technological trends (Erickson, 2012). Growing per capita GDP, population increases, and an influx of international students, have resulted in changes in class sizes and student diversity (Altbach et al., 2019; Mayhew et al., 2004). As the university student body becomes more diverse, different learning and thinking styles, which require mixed teaching approaches, are needed to allow opportunities for collaboration (Beishline & Holmes, 1997), autonomy over student-learning (Codina et al., 2020), and creativity (Zhang, 2004). While HE has traditionally focused on lecturing, a form of passive teaching, this mode of delivery is not universally suitable for all the learning and thinking styles of an increasingly diverse classroom (Beishline & Holmes, 1997; Codina et al., 2020; Zhang, 2004, 2006). Since the “Progressive Education Movement” (Dewey, 1897), educational psychology recognises the benefits of teaching methods that include active participation, experience, and reflection (Gardner, 1999; Gibbs, 1988; Kolb, 1984; Race, 2001; Sternberg & Zhang, 2014).

Physical Geography has embraced active learning techniques (Fletcher, 2005), including inquiry- (Keselman, 2003) and problem-based learning (Beringer, 2007). Geographical study incorporates fieldwork and laboratory skills, which encourages active participation; often summarised as “learning by doing” (Gibbs, 1988; Lewin & Gregory, 2019; Prosser & Trigwell, 1999). For example, France and Haigh (2018) tracked changes in fieldwork teaching and found this approach evolved from didactic models in the 1960s to recognised tools of active learning in the 1990s. Such advances match UK Quality Assurance Agency for Higher Education’s (QAA) (Quality Assurance Agency for UK Higher Education (2019)) expectations and the expectations of students, graduates, and potential employers. Benchmarks quantify student learning and ensure high standards, but for Physical Geography these can be somewhat ambiguous due to the complex, interdisciplinary nature of the subject. Fieldwork, while central to Physical Geography, is not the panacea of geographical learning and is often vulnerable due to the expense and demand of staff time (France & Haigh, 2018). To further active learning opportunities it is imperative to explore different opportunities that develop skills and knowledge without increasing resource constraints.

Technological advancements often drive improvements in learning. One example is the emergence of hydraulic modelling software (Pathirana et al., 2012). A common pattern in HE hydrology is to introduce theory and then case studies, but Pathirana et al. (2012) argue this fails to make students effective problem-solvers as the individual lacks real-life engagement with flooding. UNESCO-IHE trains teaching graduates to remove the teacher as the “expert” to encourage exchange through mixed activities and structured content. This approach is used to inspire creative problem-solving and the development of skills that are better suited to the hydrological challenges graduates face today (Pathirana et al., 2012). Another example is related to advancements in GIS-related teaching and learning (France & Haigh, 2018; Lewin & Gregory, 2019; Walshe & Healy, 2020). Historically, institutions needed specialist computers and expensive software, but with the emergence of open-source, geospatial software, such as Google Earth and QGIS, students can use personal computers at minimal costs (Van der Schee et al., 2015) which has facilitated the expansion of more inclusive GIS and remote sensing learning.

The inclination to teach through transmission, combined with the time constraints, and the occasional lack of experience in teaching with digital technologies (Van der Schee et al., 2015) means that technical skills are frequently not absorbed by students (Johnson et al., 2016; Kemp et al., 1992). Yet, technological skills taught through active learning increase chances that skills are retained (Gibbs, 1988; Revell & Wainwright, 2009; Scheyvens et al., 2008). Unfortunately, active learning exercises are not always orchestrated to benefit student learning as these are often bolt-ons, rather than planned with intent (Day, 2012). This lack of integration reduces the impact of these activities, as Revell and Wainwright (2009) found that structure is key in facilitating student learning to achieve “teaching excellence”.

Kolb’s experiential learning theory: compatibility with geography and technology

Kolb’s Experiential Learning Theory (KELT) openly engages with active learning and acknowledges individual learning styles (Kolb, 1984). It emphasises that learning is a cycle, which is expected to be repeated as learning continues (Figure 1). More specifically, experiences are transformed into learning through reflection, and in turn, these concepts feed into further choices of experimentation and experience (Healey & Jenkins, 2000; Morris, 2020). Conceptually, this framework provides structure to student learning while encouraging students to engage with their learning styles (Healey & Jenkins, 2000; Idkhan & Idris, 2021).

Figure 1. The activities appropriate for or already used in Geography that supplement the experiential learning cycle. Adapted from Healey and Jenkins (2000).

KELT is highly compatible with HE Physical Geography and is well exemplified through activities that promote experience and reflection (Healey & Jenkins, 2000). Simm and David (2002) demonstrated that problem-based learning using workshops, group discussions and fieldwork to teach research skills encouraged students to take responsibility for their learning. Further, France and Haigh (2018) showed that fieldwork has evolved from simple transmission teaching to active learning by combining classroom-based learning prior with excursions, group work, independent work, co-teaching, and previously learned knowledge and skills to promote a holistic learning experience. As well as fieldwork, KELT is compatible with technology-based learning due to its inherently interactive nature (Galadima, 2014). KELT can be applied to GIS or new emerging technology as it solidifies the development of skills, (Erickson, 2012). For example, Internet of Things (IoT) and low-cost environmental sensors (Chan et al., 2021), provide an experiential way of learning technical skills in coding and electronics, data management and statistical numeracy, as well as softer skills, and cements previous Physical Geographical learning.

IoT is rapidly evolving with every aspect of its contents (i.e. embedded sensors, software, hardware, processing ability), while costs are reducing (Singh et al., 2020). Raspberry Pi (https://www.raspberrypi.org) is readily applied in environmental monitoring, most often in wireless sensor networks (Ferdoush & Li, 2014; Kumar & Jasuja, 2017). Particle (https://www.particle.io) not only offers hardware, but also a low-cost subscription service to upload and host your data, which is appealing to environmental scientists who leave equipment in situ (Singh et al., 2020). Due to the open-source nature of IoT, when a gap in the market is discovered a solution arises quickly. For example, there is now a microcontroller available for Linux systems (Molloy, 2019) – Beagle Bone (https://beagleboard.org).

Low-cost, open-source technology is already an accessible and experiential learning tool for electronics, engineering, and computer science students (Ali et al., 2013), but the application to Geography is only just being explored. For example, Raspberry Pi was used to build robotics systems to help teach mathematical reasoning through experiential learning experiences to Geography school students (Saleiro et al., 2013). At King’s College London, the application of Particle within the FreeStation.org platform is allowing individuals to build their own environmental monitoring equipment (e.g. a weather station, rain gauge, water level sensor) to help increase the scope of data collection (Mulligan et al., 2021). Further, Cuomo et al. (2013) used Raspberry Pi to teach data collection and analysis in Geography with weather stations by using a “learning-by-doing” approach. A clear commonality in applying IoT to learning spaces is the “learning-by-doing” approach which is key to experiential learning.

Arduino: learning-by-doing

Arduino is a type of microcontroller that can be programmed using open-source code (Karvinen & Karvinen, 2011). Conceived in the early 2000s, the Arduino board was created due to the demand to encourage innovation in an educational environment (Kushner, 2011). The inventors incorporated this mentality into the ethos by promoting accessibility to electrical engineering for everyone. Arduino hardware is low-cost and circuit diagrams free to access, download, and redistribute (Blum, 2019). Arduino provides an opportunity to learn technical skills through independent exploration and engagement, which is compatible with active learning (Dougherty, 2012). A range of websites provide forums to facilitate innovation by pushing the technology into new fields. These resources promote the development of ideas while providing learning opportunities for others (Hodges et al., 2013). These online collaborations sparked a revolution amongst amateur scientists and makers, now known as the “Maker Movement” (Dougherty, 2012).

Arduino as experiential learning

Arduino is actively being analysed as a teaching tool for collaborative learning in other fields, such as engineering (Ting et al., 2020). The interactive and adaptive aspects of Arduino technology demonstrate the ability to create educational environments where learning is achieved by doing (Galadima, 2014). Active approaches to learning in Physical Geography have been evidenced and complement QAA Geography Benchmarks. Methodologies, however, are often created by staff and executed by students, reducing students’ freedom to innovate (Klein, 2003). Students can perform better when they design and execute individualised projects (Kneale, 1996; Pawson & Teather, 2002; Rossiter et al., 2017) and in Physical Geography this tends to be associated with smaller research projects as well as the undergraduate dissertation. These learning activities are often constricted by established methodologies that limit opportunities for innovation. The introduction of Arduino IoT can foster innovation in Geography research projects.

The application of Arduino in Physical Geography cements skills expected for Geographers (see Quality Assurance Agency for UK Higher Education, 2019). It develops skills traditionally outside the discipline, encouraging a wider range of technological comprehension. Such upskilling adds to universities’ competitiveness in terms of research and graduate employability at a relatively low level of investment. This paper highlights experiential learning opportunities of Arduino-based activities. Specifically, we applied Kolb’s Experiential Learning Model to exemplify the experiential learning process for undergraduate students who conducted their dissertations in 2017–2018. Further, we compare KELT to QAA benchmarks in combination with feedback from student experiences. The benefits and logistical constraints of this type of learning activity are then discussed.

Methodology

Teaching structure

Arduino-based practicals at the Geography Department of King’s College London were developed for second-year BSc Geography students by both academic and technical staff. Here we focus on the 2017–2018 academic year. There were two, 3-hour practicals which used didactic teaching to deliver one introductory session on Arduino and a second session to build a weather station (Chan et al., 2021). Lecturing and experiential learning in groups and independently were used. After the lecture, supervision was independent with students free to ask questions as needed, but peer-to-peer problem solving was encouraged. The practicals and fieldwork (Arduino deployment) were part of the module assessment which facilitated independent reflection for students. The practicals included ~20 students with varying IT skills. Previous skill sessions taught by the university included: three, 3-hour environmental data analysis computer sessions and a first-year module covering an introduction to Geographic data analysis and research design skills. Overall experience with coding and electronics was minimal as there were no previously formalised training.

Following the completion of practicals and a second-year fieldwork module, students could include Arduino in their final-year independent dissertations. Out of the ~20 initial students in the 2017–18 cohort, 7 students chose to use Arduino environmental sensors for their dissertation research. The experience of one student was used to model the learning process related to Arduino technology and Physical Geography learning. This was achieved by evaluating the relationship between QAA skills and the Arduino learning activities. This student used Arduino to construct a quality sensor (Chan et al., 2021; Pearce, 2018).

The stages undertaken during the dissertation case study are presented here (Figure 2). Firstly, independent preliminary research was conducted, relying on previously taught knowledge of hydrology and water resource management. In collaboration with research staff, a sensor was designed, constructed, and coded. Once constructed the design was tested, and following unsatisfactory results, redesigns were undertaken several times. Following a successful test further calibrations were undertaken to improve accuracy. Next, field tests were run and the sensor was deployed. If the data indicated any errors during deployment the stage could be repeated but this was not required. Following data collection, there were opportunities to obtain feedback on design, deployment, and data with staff. The final steps included data analysis and the completion of the dissertation.

Figure 2. Steps taken during the undergraduate Arduino research project, as outlined in the methodology.

Experiential learning model application

The case study methodology was mapped onto Kolb’s Experiential Learning Model (Kolb, 1984) to emphasise key learning stages that occurred during the project. Including the introductory Arduino classes for second-year Physical Geography students (Figure 3a), the Arduino project included five cycles of learning (I–V; Fig. 3b). The model shows an overview of unique exercises that require an application of different skills, but there are patterns of similarities between these exercises at each Kolb stage. For example, elements of research design (II–IV) are between abstract conceptualisation (AC) and active experimentation (AE) (Fig. 3b). Further, each cycle had a different activity for “experience” that varied between sensor construction, calibration experiment execution, field deployment, and statistical analysis, demonstrating the wide opportunities to develop different skills. Each stage can also be broken down into the skills applied in comparison to the skills expected of geography graduates by the QAA Geography subject benchmarks (Quality Assurance Agency for UK Higher Education, 2019), to identify whether this type of activity can help students effectively learn the geographical skills expected of them by universities, governments, and future employers (Figure 4).

Figure 3a. Second-year Arduino weather station practical transformed into Kolb’s stages of experiential learning. Reflective observation (RO), Abstract Conceptualisation (AC), Active Experimentation (AE), and Concrete Experience (CE).

Figure 3b. Counterpart to Figure 3a, showing the transformation of the third-year Arduino project into Kolb’s stages based on the methodology. Reflective observation (RO), Abstract Conceptualisation (AC), Active Experimentation (AE), and Concrete Experience (CE).

Figure 4. Breakdown of each stage (I–V) of the exercise methodology in Figure 3a and b, but indicating the skills learned or developed at each stage. Skills marked with an asterisk (*) indicate those that are not specifically included in the Quality Assurance Agency for UK Higher Education benchmarks (2019). Reflective observation (RO), Abstract Conceptualisation (AC), Active Experimentation (AE), and Concrete Experience (CE).

Student evaluation

To gain insight into student experience and to gauge effectiveness, semi-structured interviews (Burgess, 1984) were conducted following the completion of the third-year dissertations (King’s College London research ethics approval: MRA-21/22-28,661). The interviews had open-ended questions (Zhang & Wildemuth, 2009) that concentrated on geographical learning, student experience, Arduino, and overall skill development. The goal was to evaluate students’ perspectives and whether Arduino helped to facilitate the learning of geographical skills. The interviews were conducted online via Microsoft Teams with participants in an informal setting. Five of the seven students that used Arduino for their third-year dissertations participated. The student evaluation represents 25% of the total number of students who undertook the Arduino-based practicals and 72% of the total number of students that undertook an Arduino-based dissertation.

A list of skills was also provided to respondents and they were asked to tick “Yes”, “No”, or “Not Sure” for skills that they felt were developed associated with practicals and/or their dissertation. This was completed by four out of five of the respondents. Despite the dissertations focusing on different environmental topics, all individuals were given the same teaching, technical support, access to resources, and were working to the same marking criteria for their results. While the KELT model was only applied for one dissertation project, the learning experiences of the participants were analysed under the assumption that they would be experiencing KELT cycles during their Arduino-based dissertations.

Results

Model interpretation

Overall, the evaluated Arduino-based learning projects were compatible with the KELT model. Figure 4 illustrates a summary of the key skills applied throughout this project, relevant to each specific stage as it correlates with Figure 3b. Moreover, it demonstrates those which are specifically required in section 4 of Quality Assurance Agency for UK Higher Education’s subject benchmarks (2019). The experiential learning experience offered the development of 31 skills, with only five not specifically stated in the Quality Assurance Agency for UK Higher Education benchmarks (2019). These five skills related to applying prior knowledge from different disciplines, which included electronics, coding, physics and meteorology and hydrology. The benchmark-specific skills that were most often engaged with were: data collection (I–V) and data analysis (I–V), followed by design and planning (II–IV), which can refer to both research (II) and experiment design (III and IV). Throughout the stages, data collection – either through field or laboratory experiments – occurred six times. There were also four instances of data analysis (Figure 4). Some skills, although not mentioned in the subject benchmarks (i.e. Figure 4 item marked with *), are included as they are relevant for specific stage in research projects and encourage reflection, action, planning, or thinking.

Student experience

All respondents agreed that the initial Arduino practical was structured for effective learning (Figure 3a, stage I), and that they enjoyed the practical as a learning activity. Three of the respondents attributed their responses directly to the learning-by-doing style of the activity. All agreed that following the practical, they learned new skills and their confidence in applying Arduino-based skills increased.

Three students used Arduino to investigate air quality, one focused on water quality, and the other investigated air temperature. Two students actively chose to incorporate Arduino from the start of their projects, with the others shifting to Arduino after the project had begun. All respondents agreed that using Arduino increased their understanding of spatial data and that they applied skills learned during the initial practicals (Figure 3a, I). From the survey, 14 skills were identified as being developed during the Arduino-learning process. Of these, 7 were CE skills associated with doing an activity, while 5 were associated with active experimentation (AE; Fig. 3a,3b), (Fig 4). Only two skills (research ethics and understanding of intellectual property) were not identified as being developed by the Arduino learning experience (Quality Assurance Agency for UK Higher Education, 2019, pp. 4,9). All respondents agreed that they continued to use both technical and soft skills they learned during their Arduino experiences after graduation.

Two of five respondents indicated some prior experience with coding, but none had any experience with Arduino. Regardless, four of five respondents reported that incorporating Arduino into their third-year dissertations was challenging. The one respondent that did not find it difficult, had no prior experience with coding, and stated that “[they felt] confident with Arduino and knew [they] would get a good grade”. While most students felt that Arduino helped them learn both technical and soft skills, it was a common opinion that while the initial Arduino practical provided a foundational skillset, they did not gain sufficient knowledge to easily apply these skills to a new problem, such as their dissertation.

Three out of five respondents cited an Arduino-related element as being the hardest part of their dissertation and these respondents agreed that using Arduino created a “learning-by-failure” process that made their dissertations difficult. One respondent stated that “the [learning-by-failure] process was time consuming and led to a shift in [their] research objectives” resulting in a topic change to investigating Arduino’s potential as a scientific instrument. Following this comment, another respondent agreed that they also experienced this change due to the time-consuming, learning-by-failure process. The learning-by-failure processes clearly affected their overall experience, yet, all three recognised benefits such as a “creative approach to problem solving”, “resilience”, and “confidence”. One respondent stated, “it [Arduino dissertation] helped prepare me for life after [university], not the tech specifically, but the bigger picture”. Similarly, another respondent stated, “learning-by-failure was a large mental challenge, but [these] challenges helped [them] to problem solve creatively and increase confidence”.

All respondents agreed that the structure of the Arduino-based teaching could be improved. Three respondents agreed that changes to the structure of the teaching, e.g. starting in the first year, could help to improve the Arduino student learning experience. While two respondents agreed that more focus on the coding would improve the student learning experience. Generally, more time covering the basics of the software, coding, and hardware would be beneficial, especially since the engagement and enjoyment (i.e. the learning-by-doing style and the practical application) was reported as a positive experience.

Discussion

Overall, the Arduino-based learning project translated well to the KELT model, evidenced by the case study and the student evaluations. Illustrated in Figure 4, the Arduino-based project facilitated learning of key Physical Geographical skills in an experiential format. Despite skills taught in the second-year class being relatively basic (Figures 3a, I and Figure 4, I), the opportunity to explore ideas and conceptualise introductory theory, facilitated by the accessibility of online platforms, allowed students to actively experiment with Arduino. This experimentation continued the development of a range of skills (e.g. electronics and coding), through reflection on attempts to test applications and explore the implementation of a water quality sensor (Figure 4, II). The repetition of experience and reflection further allowed these skills to be embedded into student knowledge (Healey & Jenkins, 2000; Morris, 2020). Moreover, this project is a clear example of a “concrete experience” (Morris, 2020) because “learners are involved and active … exposed to novel experiences, which involves risk; learning demands inquiry to specific real-world problems; critical reflection acts as a mediator of meaningful learning”. The concrete experience was again evidenced from the dissertations of all respondents where Arduino-based learning was new. Further, two respondents specifically indicated the process had a “learning-by-failure” approach, which indicates an awareness of risk. All students had applied their Arduino-based learning to practical environmental issues, and the overall agreement that “creative approaches to problem solving” was learned during this process. Clearly this indicates the important role of reflection throughout.

The common elements of design and planning in each stage (II–IV) provided repetition of similar skills. The application of innovative thinking and theoretical knowledge promoted the development of skills in problem-solving and analysis, key skills in the benchmarks (Quality Assurance Agency for UK Higher Education, 2019, p. 9). As the design element differs, the process appeals to different types of learners. For example, the sensor design (II) appeals to more creative and problem-solving approaches; while calibration tests (III) fits more to process-inclined learners. This fits Kolb’s theory that experiential learning is compatible with more than one style of learner, which is crucial now that a more diverse range of learner styles need to be addressed in these larger HE classes (Healey & Jenkins, 2000; Klein, 2003; Kolb, 1984; Kolb & Kolb, 2017).

In this study, dissertation projects gave opportunities to reinforce skills that were taught in previous years. For example, in cycle III (see, Figure 4) during the calibration tests, and cycle V during the independent application of statistics. In the latter example specifically, the choice of a more non-traditional project allowed for active experimentation with more non-traditional statistical methods that were the choice of the student (Pearce, 2018), improving student engagement with learning (Evans & Boucher, 2015; Scheyvens et al., 2008). This, in turn, helps to develop skills in “numeracy and statistical literacy” and data visualisation (Quality Assurance Agency for UK Higher Education, 2019, p. 8). As these exercises solidify skills and knowledge fundamental to Physical Geography, they complement other learning objectives within a curriculum. For example, the planning stages of the project would have been unpredictably longer without the foundational knowledge. The planning stages (II) and the final writing stage (V) allowed prior subject theory to be reflected upon and applied, while developing written communication; each which are Benchmarks associated with studying Geography (Quality Assurance Agency for UK Higher Education, 2019, p. 9). A key skill in the QAA Benchmarks is “taking responsibility for learning and reflection upon that learning” (Quality Assurance Agency for UK Higher Education, 2019, p. 9). Therefore, a crucial link between Arduino and Physical Geography learning objectives is an approach which empowers students to take responsibility for their own learning experiences through reflection and application.

The student evaluations strongly evidence the compatibility of Arduino-based learning exercises with Geography benchmarks. The agreement of the respondents in learning QAA specified skills while undertaking their Arduino projects is important as it gives an indication of the potential positive effects that teaching structure (i.e. practicals and then independent application) and method (i.e. Arduino technology) have on student learning of Physical Geography skills. Overall, student experience strongly evidenced that Arduino is a form of experiential learning as the respondents themselves, while not specifying the stages by name, described KELT stages throughout their learning: observe (RO), think (AC), plan (AE), and do (CE). Two respondents explicitly pointed out the “learning-by-doing” structure of the second-year practical and two others could recognise this style of learning being repeated during their third-year dissertations. The skills in which all respondents agreed they had developed from undertaking this learning approach showed a trend towards skills that were associated with concrete experience or the “do” Kolb stage (i.e. fieldwork, data collection, laboratory skills). While KELT emphasises the importance of each learning stage (Kolb, 1984; Kolb & Kolb, 2017), emphasis is given on experience from the literature (Morris, 2020).

Importantly, all respondents agreed that they applied skills from the second-year practical to their third-year dissertations, which evidences the repetition of skills and learning across years and thus the cycles of learning key to KELT (Morris, 2020). The cyclical nature of learning, and the impression of the continuity of learning throughout life were also reported indicating that both technical skills and soft skills learned were then applied to new scenarios after their degree finished. Interestingly, two respondents also identified skills they felt they had developed that were not asked in the survey. These included “confidence” and “understanding of the research process”. The latter does not necessarily translate directly to “research design”, but does relate to all stages of conducting scientific research: collaborating with research staff, time management, ethical and scientific integrity, independent decision making, as well as structuring research projects. The development of these skills suggests everyone had their own unique learning experience (Idkhan & Idris, 2021; Kolb & Kolb, 2017), reflecting the importance of adaptable learning methods to maximise benefits to multiple individuals.

To fully assess whether Arduino is potentially a means to teach technical skills to a wider diversity of students, the limitations of Kolb’s theory and experiential learning, the methods applied here, and with Arduino must be considered. While these models are a balanced visualisation, time is not equally shared between the four learning stages in each cycle. For this project, AE and CE dominated the timeframe, especially in the initial design and construction stages (II). Whereas Kolb’s theory emphasises the value of all four parts of the cycle, the experience and experimentation of stage II alone cannot ensure learning without the other stages (Healey & Jenkins, 2000; Kolb & Kolb, 2017). Therefore, to have regular stages of reflection, a work log or reflective diary would promote better balance of the cycle. Although, it is important to note that within stage II, through the process of trial-and-error with design and construction, there is an underlying complete KELT cycle taking place which requires observation and reflection.

Arguably, the KELT model is incomplete at representing the interconnectedness of this learning process. For example, the simplification in Figure 3a does not consider the input of collaborative group work. While Kolb’s model (1984) has been perceived by some as making the individual’s learning a solitary process (Seaman, 2008; Vince, 1998), more recent thinking suggests that experiential learning is fundamentally collaborative and could be viewed as being “relationship-centred” rather than “student-centred” (Tomkins & Ulus, 2016). Regardless, identifying learning exercises, such as the development and use of low-cost sensors, which appeal to multiple learning styles and meet wider geographical skills stated in benchmarks (Quality Assurance Agency for UK Higher Education, 2019), would benefit Physical Geography.

While the feedback from student experience has been able to demonstrate that using Arduino-based learning activities helped to teach some Physical Geography skills, including, “intellectual and subject specific”, “generic”, and “personal attributes and social skills” (Quality Assurance Agency for UK Higher Education, 2019, pp. 8–9), there could be improvements. The respondents were met with obstacles during their dissertations that could be directly traced to Arduino and an insufficient technical skillset that the second-year practical did not adequately address. The obstacles, and more jarringly, the “learning-by-failure” approach is not necessarily beneficial or effective for all students and could be directly damaging for some (Maltese et al., 2018). The improvements suggested by respondents, however, agree with our reflections: a more integrated and holistic structure could have the benefit of increasing the effectiveness of this method for students learning Physical Geography skills. Moreover, any changes in structure must also be accompanied by a more thorough assessment of student perspectives on learning, to better assess the effectiveness of this method. For example, a survey conducted before any Arduino practical has taken place, and repeated surveys following each practical until completion of their degree (if Arduino-based learning has continued throughout).

Wider application in universities …

Although experiential learning impact is hard to quantify, this Arduino-based learning activity does oppose the domination of transmission learning in HE (Day, 2012) and clearly offers an experiential alternative that is compatible with multiple learning styles. The benefits of the implementation of this type of exercise are experienced at an individual and university level. Having students who are collecting data at higher spatial and temporal resolutions as they are making their own environmental sensors is advantageous to university research groups, especially considering the low investment cost. Further, the branching of geography into the field of electronics and programming helps to enhance the discipline’s STEM qualifications, opening opportunities for funding (Erickson, 2012; Head & Rutherfurd, 2021; Al Mamun et al., 2015; Royal Geographic Society, 2011). While this type of activity cannot replace the demand for GIS-skilled graduates, it does impart different, yet valuable technological skills, particularly in coding.

… and a more realistic view

While Arduino-based activities hold great potential for individual learning, some limitations must be taken into consideration. The explorative nature of the technology is an opportunity for students to engage, develop new skills, and problem solve, but the more realistic basis of Arduino is learning through failure. Trial-and-error learning has the benefits of developing abstract thinking and problem-solving (Nelson, 2008). The process, however, is time consuming, and while the assumption is that the eventual success will encourage engagement and solidify learning, the bias towards failure with Arduino-based projects is not effective for some students and can have a negative impact (Maltese et al., 2018). One respondent specifically stated that undertaking this learning-by-failure at a stressful time was a mental challenge, and with increasing numbers of mental health problems among HE students (Duffy et al., 2019), the introduction of an exercise that trends towards learning-by-failure should be mitigated with a supportive and holistic learning structure. The issue can be overcome with introductory classes to teach basic skills while prioritising the freedom of choice for students by having optional skills-based modules, allowing them to retain responsibility for their learning (Day, 2012; Quality Assurance Agency for UK Higher Education, 2019, p. 9) and encouraging engagement, a key driver of learning (Biggs & Tang, 2011).

Another limitation are resource costs. Due to the learning-through-failure style, the projects can be time consuming, and even the most organised student cannot predict the time requirements for each stage of the project. In that sense, it can be unsuitable to highly time-constrained, traditional module structures and assessment regimes. This water quality example was conducted within the context of a 60-credit dissertation, which ensured the time investment would be returned in weighted credit value within the programme. Within traditional programme structures, it would be more realistic to have first-year introductory classes to build the skills required, include a smaller research project in the second year, and the option of dissertation research to continue the overarching cycle of experiential learning that continually applies the skills discussed. Finally, individual parts are low-cost but few Physical Geography departments have a specific lab or research group looking at low-cost environmental sensors which means there is little data on overall investment and costs of maintenance.

Currently, the impact such active and experiential activities have on student learning is unclear and there is a need for more evidence (Day, 2012). This example further emphasises the need for more research into student learning, especially in HE. An investigation into how graduates perceive their learning and how well they have learned would help to benefit the case for experiential learning (Healey & Roberts, 2004). In terms of this paper, a limitation is the ability to differentiate between skills that are obtained and reinforced by choosing an Arduino-based project and those which are solidified during a regular dissertation module. This issue arises as third-year geographical dissertations are specifically designed to apply skills undergraduates have learned in previous years. To overcome this, a comparative study could be conducted with students who have previously completed Arduino projects and regular geographical dissertations to see the similar and unique skills each project reinforces.

Conclusion

Learning through experience is integral to Physical Geography. The dominant mode of learning, however, is still lecturing, thus, HE requires new learning approaches as the demographics of universities change. Therefore, the inclusion of new learning exercises that are beneficial to individual learning experience and are compatible with evolving trends in universities, without radically changing course content, are needed. This paper has investigated whether Arduino-based projects can be considered an affordable, accessible, learning tool in Physical Geography that encourages learning-by-doing, through an example of an Arduino-based water quality project. The use of Kolb’s model to compare the project’s compatibility with experiential learning has demonstrated that Arduino is an experiential learning tool. The exercise’s compatibility with geographical skills, evidenced by the comparison to national subject benchmarks (Quality Assurance Agency for UK Higher Education, 2019) suggests it is a useful learning tool for undergraduate Physical Geographers to learn the skills expected of them. Importantly, insights from student evaluations have demonstrated a positive response for this method in learning Physical Geography skills. This method, however, is not without its challenges, so must be supported further by changes to course structure to supplement and strengthen learning of new technical skills to increase student confidence in applying these skills independently. Furthermore, the technology’s low cost and accessibility provides an opportunity for Physical Geography departments to produce technically skilled students without large investment costs, while attaining research benefits in terms of data and eligibility for more STEM funding (Erickson, 2012; Galadima, 2014).

Despite the lack of quantification on the effectiveness of active learning due to a small sample size for student evaluations, this Arduino-based practical has shown that geographers can lead the way in embracing the advantages of low-cost technology. It is suitable for addressing the demands of modern classes: larger sizes and more variability in learning styles, while also teaching new, in-demand technological skills in coding, electronics, and programming, as well as reinforcing key Physical Geography skills that are taught in the wider degree and are expected from the benchmarks.

Disclosure statement

No potential conflict of interest was reported by the author(s).