Operational Issues

1a: An understanding of the constraints and benefits of different technologies

As a lecturer at the University of Greenwich, I am actively engaged in supporting students and colleagues in the effective use of digital technologies to enhance teaching, learning, and research in the fields of accounting and financial management. My responsibilities extend beyond classroom teaching; I also provide consultancy to colleagues on how to integrate technological tools into their syllabi, tailoring advice to meet their specific objectives. For instance, I am frequently approached for guidance on embedding Bloomberg into teaching practice and for support in gathering financial data for research projects. In my modules Financial Business Intelligence and  Business Analytics I, I have designed the content to embed a range of digital tools that enable students to apply analytical approaches to real-world business problems. Students are guided through processes such as problem identification, data collection, and data preparation, which include key machine learning steps such as data cleaning, clustering, and retrieval. They are also trained to use Bloomberg and Microsoft Excel as part of the Bloomberg Global Challenge, which provides an authentic experience of stock trading and collaborative decision-making using virtual funds. To maintain my own expertise, I attended Bloomberg workshops in London, including training for the Bloomberg Spreadsheet Analysis certificate. These events allowed me to liaise with Bloomberg account managers for updates on emerging functionalities.

The integration of Bloomberg and STATA reflects wider trends in financial education. Bernacki et al. (2020) highlight the increasing role of digital tools in handling unstructured financial data and applying regression analysis, equipping learners with industry-relevant skills. Complementing these, I have adopted Microsoft Sway and Mentimeter to enrich engagement. For example, in Business Analytics I, Sway was used to present module expectations in an accessible format, supporting visual learners at the start of spreadsheet-based modelling activities. Studies by Harefa et al. (2019) and Wihartanti & Wibawa (2017) demonstrate the value of such multimodal delivery for enhancing learner autonomy and ownership. Despite their benefits, these tools present challenges. Bloomberg and STATA require significant onboarding, particularly for students with limited prior exposure. Meanwhile, Sway, while effective for reflection and storytelling, lacks advanced analytical capability. To address these challenges, I adopt a dual tool approach: using Sway for reflective and narrative activities while employing Excel and STATA for rigorous data analysis. I also introduced Mentimeter live polling in Management Accounting (ACCO1084) to encourage anonymous participation, ensuring an inclusive learning environment and reducing barriers for students who may feel hesitant to contribute in large groups. This approach reflects my commitment to understanding the interplay between technology and learning by using digital tools to enhance accessibility and participation for all learners (CMALT Value: Understanding the interplay between technology and learning).

As a lecturer at the University of Greenwich, I actively integrate digital technologies into teaching and learning to enhance students’ practical skills in accounting and financial management, demonstrating my commitment to understanding the interplay between technology and learning. My engagement extends beyond classroom instruction, as I frequently provide consultancy to colleagues on embedding technological tools into their curricula, particularly Bloomberg and statistical software such as STATA and SPSS, which reflects my commitment to sharing effective practice. Evidence of the impact of this approach is visible in the performance of my students in professional competitions. For example, in the Bloomberg Global Challenge 2024, 33 Greenwich teams competed globally, and two of my supervised teams ranked within the top 250 worldwide: SANDBOX (MSc Finance and Investment) ranked 172, and D.R.A.N.S (MSc Fintech and Finance) ranked 227. These achievements illustrate students’ practical application of complex financial data and analytical tools, aligning with my commitment to supporting learners through innovative and authentic use of technology and CMALT values..

Further evidence of impact includes a student survey indicating an 82% increase in confidence using Bloomberg and unsolicited student requests for additional guidance on Bloomberg functions and terminal access. These responses highlight the effectiveness of my teaching in building digital competence and confidence, demonstrating my empathy and willingness to learn from and respond to learners’ needs. The modules I teach, including Financial Business Intelligence (COMP1238) and Business Analytics I (COMP 1934), are structured to provide active engagement with digital tools. Students progress through stages of problem identification, data collection, preparation, and analysis, incorporating machine learning techniques such as data cleaning, clustering, and retrieval. By integrating Bloomberg, Excel, and STATA, students develop the ability to work with real world financial data, conduct descriptive, correlation, and regression analyses, and create interactive dashboards. To complement these activities, I use Mentimeter to enhance engagement, offering multimodal learning experiences. This practice reflects my commitment to staying current with technological developments and my ability to align innovative tools with sound pedagogical principles, ensuring an inclusive and engaging learning environment.

This work underscored the importance of scaffolding in technology integration and reflects my commitment to understanding the interplay between technology and learning. The complexity of Bloomberg and STATA necessitated carefully designed onboarding strategies, including asynchronous guides, live demonstrations, and drop in clinics. Student feedback confirmed not only an increase in confidence but also a stronger sense of inclusivity, which aligns with my empathy and willingness to respond to learners’ needs. Moving forward, I plan to implement adaptive feedback systems and explore AI driven analytics support to further reduce barriers while maintaining academic rigour. This iterative approach demonstrates my commitment to staying up to date with technological developments while ensuring that learners are supported effectively. This teaching experience has highlighted both the benefits and constraints of digital technologies and reaffirmed the CMALT principle of understanding the pedagogical implications of technology integration. Tools such as Bloomberg and STATA offer significant analytical power, yet they require a structured onboarding process to ensure equitable access. In my classes, students come from highly diverse educational backgrounds and age groups, resulting in varying levels of digital literacy and prior exposure to technology. Some students initially struggled with core skills, such as using a keyboard,  navigating Bloomberg terminals, and manipulating Excel data. To address these challenges, I implemented a multi layered support system that included step by step guidance, live workshops, asynchronous learning resources, and one to one drop in sessions. This approach encouraged reflective learning, enabling students to assess their progress, build confidence, and gradually develop mastery of complex analytical tools. These strategies demonstrate my commitment to empathy and inclusive practice, ensuring that all learners, regardless of prior experience, can participate meaningfully.

Integrating multiple tools presented operational challenges, particularly in the Business Analytics I module. Students were required to download and clean economic and financial data from Bloomberg, classify it by industry, adjust data frequencies, and upload datasets into STATA or SPSS for statistical analysis. Teaching students how to handle outliers, create dummy variables for years and industries, and perform descriptive, correlation, and regression analysis involved intricate processes. Through continuous scaffolding and iterative feedback, I observed students developing confidence and analytical fluency, which aligns with my commitment to evidence informed practice as recommended in current TEL research (Bernacki et al., 2020; Rapanta et al., 2021). These findings also reinforce my commitment to sharing effective practice, as I have disseminated these approaches in internal faculty development sessions.

The use of Mentimeter and Kahoot further illustrated the value of inclusivity and flexibility in digital learning environments. Mentimeter and Kahoot provided accessible, visually engaging content that helped clarify module expectations, supporting learners with varied preferences and abilities. Both platforms allowed anonymous participation, ensuring that even students with lower confidence or limited digital proficiency could engage without fear of judgment. These strategies demonstrate my commitment to inclusive and collaborative practice, fostering equity and encouraging active participation from all learners. I also implemented structured group work to promote peer support, pairing students with learning difficulties, such as dyslexia, with peers who could provide immediate assistance. This practice created an environment where collaborative and reflective learning thrived, aligning with my empathy and willingness to learn from and with colleagues and learners. One of the most significant lessons I have learned from this experience is the critical role of scaffolding and progressive exposure when integrating complex digital tools. Student feedback indicated that introducing technologies in small, manageable steps supported by opportunities for practice, feedback, and discussion was far more effective than presenting all tools at once. Interactive platforms such as Kahoot and Mentimeter were invaluable in this process, enabling immediate formative feedback and reinforcing understanding engagingly. These strategies illustrate how my approach aligns with the CMALT principle of understanding technology’s impact on pedagogy, as I continuously adapt teaching design to enhance learner engagement and confidence.

Collaboration has also emerged as a key factor in successful technology integration. Students who worked in groups often explained analytical processes to their peers, reinforcing their own understanding while supporting others. This observation highlights how technology, when combined with structured peer interaction, becomes a catalyst for social and cognitive engagement rather than a purely technical skill set. This reflects my commitment to fostering collaborative learning environments as part of the CMALT framework. Another critical insight relates to balancing rigour with accessibility. While tools like Bloomberg and STATA demand advanced analytical capabilities, adopting inclusive strategies such as anonymous polls, structured demonstrations, and peer assisted learning ensures that all students have equitable access without compromising academic standards. This balance demonstrates my commitment to using technology to enable learning, not create barriers, aligning with CMALT values of inclusivity and learner focused practice. Looking ahead, I intend to integrate AI driven analytics tools such as Qlik Sense and advanced Excel features to provide adaptive support for learners. These tools will enable anomaly detection, predictive modelling, and automated dashboard generation, allowing students to focus on higher order interpretation and decision making rather than time intensive data preparation. I also plan to enhance formative assessment strategies by embedding interactive tools, including Mentimeter and Kahoot, at key stages in the learning process to provide real time feedback and personalised interventions. From a collaborative perspective, I aim to expand structured peer led workshops and group projects, particularly to support students with diverse learning needs, reinforcing the CMALT principle of sharing effective practice and promoting inclusive engagement.

Ongoing professional development will remain central to sustaining this progress. Attending specialist workshops, engaging with technology providers, and reviewing current research will allow me to maintain currency and continuously refine my practice. This reflects my commitment to keeping up to date with emerging technologies and evidence informed approaches, ensuring a sustainable and innovative teaching model. My experience integrating Bloomberg, STATA, Excel, Sway, and Mentimeter has demonstrated that effective technology use is as much a pedagogical challenge as it is a technical one. Scaffolding, inclusive design, and collaborative learning structures enable students from diverse backgrounds to engage meaningfully with complex financial data. The reflective insights gained from this experience inform current practice and future enhancements. By grounding my work in the CMALT principles of inclusivity, reflection, collaboration, and continuous improvement, I ensure that technology acts as an enabler of deep learning and employability, preparing students for both academic success and professional advancement.

References

• Association for Learning Technology (ALT). (2021) CMALT: Certified Membership of ALT – Guidelines for Candidates and Assessors. Available at: https://www.alt.ac.uk/certified-membership.

• Bernacki, M., Greene, J., & Crompton, H. (2020). Mobile technology, learning, and achievement. Contemporary Educational Psychology, 60.

• Harefa, S., et al. (2019). The effectiveness of Microsoft Sway in enhancing student engagement. Journal of Educational Technology.

• Rapanta, C., Botturi, L., Goodyear, P., et al. (2021). Balancing technology, pedagogy and the new normal. Postdigital Science and Education, 3(3), 715–742.

• Saubern, R., Taylor-Guy, P., & van der Keij, F. (2022). Education Technology Value Evaluation Tool for Schools. ICAL.

• Wihartanti, L., & Wibawa, B. (2017). Multimedia in Accounting Education: Lessons from Indonesia. International Journal of Education.

1b: Technical knowledge and ability in the use of Learning Technology

My technical expertise spans a wide range of learning technologies, including Excel, STATA, Qlik, Bloomberg, Python, and R, which I have systematically integrated into my teaching practice. This breadth of expertise reflects my commitment to staying up to date with technological developments and continuously expanding my digital capability. In Business Analytics I, I use Qlik dashboards combined with Bloomberg datasets to provide students with hands on experience in visualising complex financial information, performing comparative analyses of stock performance, assessing portfolio risk, and applying theoretical concepts in practical, industry relevant contexts. These tasks are designed to foster both technical proficiency and analytical thinking, illustrating my commitment to understanding the interplay between technology and learning. By enabling students to manipulate real world datasets, I aim to create authentic, work relevant learning experiences that align with professional and job market expectations, ensuring that technology serves as an enabler rather than a barrier.

The impact on students has been significant. Feedback from Qlik, Bloomberg, and Excel sessions consistently indicates that students perceive these skills as directly enhancing their employability. Many report including these competencies on their CVs and being asked about them during job interviews, with particular emphasis on Excel functions such as VLOOKUP and INDEX MATCH. One student explained that confidence with these formulas enabled them to complete a complex financial modelling project, which later became a focal point in an interview. Observing students progression from initial apprehension to confidently applying advanced formulas and building dashboards has reinforced my understanding that digital tools must be contextualised through structured pedagogical support. This experience aligns with my commitment to reflective practice, as I continually adapt teaching strategies to improve inclusivity and engagement. In future iterations, I plan to introduce smaller, incremental milestones within the module to help students monitor progress, develop confidence, and explicitly connect their learning to employability outcomes. This reflective adjustment supports the CMALT principle of evaluating technology’s impact on learning and refining approaches based on evidence informed practice.

At the postgraduate level, I provide targeted guidance in STATA for regression analysis and econometric modelling, particularly supporting Level 7 dissertation students. Many arrive with minimal prior experience of advanced statistical software, presenting a significant barrier to independent learning. To address this, I developed a comprehensive STATA tutorial workbook and offered weekly one to one clinics where students could test scripts, troubleshoot errors, and engage with advanced analytical concepts. In some cases, students struggled with basic IT fundamentals, such as keyboard shortcuts or navigation, which further complicated their ability to progress. To mitigate this, I created mixed ability peer groups, enabling less confident learners to collaborate with peers while still receiving personalised support. This strategy reflects my empathy and willingness to learn from and with others, as well as my commitment to fostering collaborative approaches to support learning. While this approach was highly effective in improving student outcomes and confidence, my reflections suggest that supplementing peer learning with structured asynchronous video tutorials would further strengthen accessibility and allow students to learn at their own pace. This insight demonstrates how critical reflection informs future enhancements in my practice. Supporting colleagues in adopting learning technologies has also become a key aspect of my work, reinforcing my commitment to sharing effective practice across professional networks. For example, three colleagues expressed interest in integrating Business Analytics into their teaching. One stated, “I am very much interested in exploring Business Analytics” and requested access to my module Moodle page as a reference. In response, I provided bespoke training sessions, illustrating how analytical tools can bridge theoretical finance concepts with practical data handling techniques. This collaborative work not only built colleagues confidence but also contributed to the University of Greenwich's strategic goals for digital innovation. Such professional collaboration reflects the CMALT value of maintaining a commitment to personal and professional development, as well as the principle of supporting others through sharing expertise. My reflections indicate that formalising this support into structured workshops and creating an online knowledge hub would create a sustainable framework for peer learning, extending the impact beyond individual consultations.

Challenges have inevitably arisen. Several students and colleagues requested guidance on downloading large datasets from Bloomberg or applying Python scripts for automation and sentiment analysis. These advanced tasks required step by step demonstrations and personalised support sessions. Differences in prior digital experience also affected the pace of learning, with some learners requiring significantly more time to master complex concepts. To overcome this, I integrated peer support alongside individual follow ups, ensuring that every student could progress without disengaging. This experience reinforced my understanding that flexibility, adaptability, and empathy are essential when teaching technical skills, as learners needs vary widely. Reflecting on these challenges, I plan to introduce pre session diagnostic assessments to identify skill gaps earlier, enabling me to provide differentiated support strategies and ensure smoother progression for all learners. This adaptation aligns with my commitment to evidence informed and learner centred approaches, as outlined in the CMALT principles. Reflection on technology selection has also been critical. During my PhD, I extensively applied Python, developing web scraping scripts to collect large scale Twitter datasets and conducting sentiment analysis on annual reports and social media disclosures. I also utilised NVivo and LancsBox for textual analysis. These experiences provide me with authentic examples to share with students, demonstrating the real world application of coding and analytics in financial research. However, I recognise that advanced tools such as Python can overwhelm learners if introduced prematurely, especially in our business faculty.  As a result, I plan to sequence technology exposure more carefully in future, starting with familiar platforms like Excel and Qlik before progressing to programming based tools. This incremental approach supports my commitment to designing learning pathways that reduce cognitive load while maintaining academic rigour, reflecting the CMALT principle of understanding the pedagogical implications of technology integration. Professional development remains central to my practice and illustrates my commitment to keeping up to date with emerging technologies. I have completed Bloomberg’s Spreadsheet Analysis training and earned advanced Excel certifications, ensuring that I remain current with industry expectations. This enables me to model best practice for both students and colleagues, promoting a culture of digital confidence across the institution. Looking ahead, I plan to consolidate resources into an online repository of Qlik and STATA materials for staff and students. This initiative aligns with my commitment to sharing effective practice and creating sustainable impact by supporting independent and collaborative learning beyond my classroom.

My transition from being primarily a user of learning technologies to a mentor and trainer has reinforced an essential truth: technical expertise achieves its greatest impact when combined with pedagogical insight. Students often begin with apprehension, but through scaffolded learning, personalised clinics, and peer assisted strategies, they consistently develop confidence, competence, and career ready skills. Similarly, colleagues who have engaged with my support demonstrate greater confidence in adopting TEL practices, enhancing module innovation and student engagement across the Greenwich Business faculty. Reflecting on these experiences, I have learned that successful technology integration requires balancing ambition with accessibility, adapting to diverse learner needs, and maintaining a cycle of reflection and evidence based improvement. By embedding advanced analytical tools, providing structured support, and fostering collaborative professional development, I ensure that my practice aligns with all four CMALT values, understanding the interplay between technology and learning, staying up to date with technology, empathy and collaboration, and sharing effective practice. These principles remain central to my approach, ensuring that technology enhances learning, promotes inclusivity, and supports both academic and professional success.

References

• Association for Learning Technology (ALT). (2021) CMALT: Certified Membership of ALT – Guidelines for Candidates and Assessors. Available at: https://www.alt.ac.uk/certified-membership.

• Kurt, S. (2018). TPACK: Technological pedagogical content knowledge framework. Educational Technology, 58(5), 1–11.

• Lave, J., & Wenger, E. (2001). Supporting lifelong learning: A guide to theory, practice, and policy. Routledge.

• LancsBox. (n.d.). Text analysis and corpus linguistics software. Lancaster University.

• NVivo. (n.d.). Qualitative data analysis software. QSR International.

Evidence 1: Four screenshots of my students' work using Qlik