Aniekan Essien currently is an Assistant Professor in Business Analytics at the University of Bristol Business School. Aniekan's research interests encompass artificial intelligence (Deep Learning) for time-series forecasting, Information Systems (Business Informatics), Databases and Data Mining/Science.
Leveraging Analytics to Enhance Personal Tutoring Outcomes: Opportunities and Challenges
In this article we are casting the spotlight on the increasingly influential role of personal, or academic, tutoring in shaping the academic journey of university students. Personal (or academic) tutoring is now a key aspect of the university system, providing students with the support they need to succeed in their academic journey. Given the rapidly evolving educational landscape today, the role of the personal tutor is even more important (Wakelin, 2022). “So, what exactly is personal tutoring”, you ask? Think of it as a bespoke, one-on-one academic relationship between a student and a tutor. These dedicated tutors serve as the guiding compass on a student's academic journey, offering not just academic support but also contributing to overall well-being and pastoral care (Roldán-Merino, Miguel-Ruiz, Roca-Capara, & Rodrigo-Pedrosa, 2019). Universities are much more than simply giving out degrees, but are also hubs where critical life skills are honed, which is where personal tutors come in. With their expertise, they shed light on important aspects such as coursework understanding, study skills, and time management, equipping students with the resource they need to ace every academic challenge. Given the rise of online education (during and after COVID-19) and the increasing competition for academic success, students need support more than ever before (Koksal, 2020).
In today’s data era, integrating analytics into personal tutoring has the potential to transform the way tutors approach their role. Analytics simply refers to the use of data and statistical methods to gain insights and inform decision-making (Evans & Lindner, 2012). In the context of personal tutoring, analytics can provide personal tutors with valuable insights into student performance, allowing them to track progress, identify patterns and trends in behaviour, and tailor their support to meet the individual needs of each student (Arroyo et al., 2014; Zhang, Basham, & Yang, 2020). This allows them to tailor their support strategies to the individual needs of each student. The result? A personalised tutoring approach that catalyses more effective learning and superior academic outcomes. In recent years, the field of personal tutoring has seen a significant increase in the adoption of data analysis tools, commonly referred to as analytics (Viberg, Hatakka, Bälter, & Mavroudi, 2018).
The use of analytics in personal tutoring has been shown to increase retention rates (Pulker, 2019), graduation rates, and grades, demonstrating its value in improving student success. With its ability to provide data-driven insights and inform decision-making, analytics is rapidly gaining popularity in the field of personal tutoring.
The Benefits of Analytics in Personal Tutoring
So, what are the benefits of using analytics in personal tutoring and how can it impact student outcomes? With the increasing competition for academic success and the rise of online education, personal tutors play a vital role in helping students navigate their academic pursuits. By leveraging the transformative power of analytics, personal tutors can provide students with even more targeted and effective support.
Impact on student success
One of the key benefits of analytics in personal tutoring is its ability to impact student success. Analytics provides personal tutors with a deeper understanding of student performance, allowing them to track progress and identify areas where students may need additional support (Lowes, 2020). By using data-driven insights, personal tutors are better equipped to adjust their tutoring strategies, resulting in a more personalised and effective approach to tutoring. For example, if analytics reveals that a student consistently struggles with a particular topic, a personal tutor can focus their support on that area, providing the student with the additional help they need to succeed (refer to Figure 1).
Identifying patterns and trends
In addition to impacting student success, analytics can also help personal tutors identify patterns and trends in student behaviour. By tracking student performance over time, tutors can identify areas where students are struggling and adjust their support accordingly. For example, if a student is consistently missing assignments or struggling with a particular subject, personal tutors can use this information to provide additional support and resources to help them overcome these challenges (Lowes, 2020).
Improved Retention Rates, Increased Graduation Rates, and Higher Grades
There is evidence in the existing literature in favour of the use of analytics in personal tutoring, resulting in improved retention rates, increased graduation rates, and higher grades (Hernández-de-Menéndez, Morales-Menendez, Escobar, & Ramírez Mendoza, 2022). For example, some studies have found that students who receive personalised support through personal tutoring are more likely to stay in school, graduate, and perform better academically (Robinson, Kraft, Loeb, & Schueler, 2021; US Department of Education, 2017). In another study by Arroyo et al. (2014), the authors found that students who received personalised support, including analytics-driven tutoring, were more likely to persist in their studies and achieve higher grades than those who did not receive this type of support. By providing personal tutors with data-driven insights, we argue that it makes them better equipped to identify areas where students may need additional support. A real-world example of this is a study conducted by the University of Leeds in the UK, which found that students who received personal tutoring support were more likely to complete their studies and achieve higher grades.
Tailored Support Based on Student Needs and Preferences
Finally, the use of analytics in personal tutoring enables personal tutors to tailor their support based on student needs and preferences. By using data-driven insights, personal tutors can better understand the learning styles and needs of individual students, leading to a more personalised approach to tutoring. This can result in a more effective and engaging tutoring experience for students, leading to improved learning outcomes and increased student satisfaction. For example, a study conducted by the University of Pennsylvania found that students who received personalised tutoring support were more engaged and motivated in their studies, leading to improved learning outcomes (Karumbaiah, Baker, Tao, & Liu, 2022).
Enhancing Student Engagement through Analytics
With the integration of technology into education, personal tutors can leverage analytics to enhance student engagement, leading to improved outcomes for students. Analytics can provide valuable insights into student engagement and learning behaviour, helping personal tutors to identify areas where students may be struggling or need additional support. By using data-driven insights, personal tutors can make informed decisions about the best ways to support students, tailoring their approach to meet the unique needs of each student. In addition, analytics can also help personal tutors to understand which learning activities and methods are most effective, allowing them to design lessons that are engaging and impactful. Consider a study conducted by Purdue University which used analytics to track students' progress, identify their struggles, and help provide additional support as required. Armed with data-driven insights, personal tutors can make nuanced decisions on how to best support individual students, personalising their approach to the unique needs of each student.
Tutors can use analytics to provide regular feedback to students, helping them to track their progress and understand where they need to focus their efforts. By providing students with regular feedback, personal tutors can motivate students to engage more fully with their studies, encouraging them to take an active role in their learning journey. Digital learning activities such as online quizzes or interactive simulations can also be monitored using analytics. By gauging student engagement with these tools, tutors ensure that students remain active in their learning and receive all the necessary support.
One way in which personal tutors can enhance student engagement is through the use of gamified learning activities. Gamification refers to the integration of game-like elements into learning activities, providing students with an engaging and dynamic learning experience (Dichev & Dicheva, 2017). For example, personal tutors can create gamified activities that provide students with instant feedback and rewards for their efforts, helping to keep them motivated and engaged (see Figure 2). A typical example where tutors can create instant feedback and reward-driven activities, such as those implemented in Duolingo's language learning system, to keep students motivated and immersed. Personalised learning paths, which provide students with a tailored learning experience based on their individual needs and preferences, can also be effective in enhancing student engagement (Shemshack & Spector, 2020). Personalised learning paths can help students to stay focused and motivated, reducing the likelihood of disengagement and promoting a more positive learning experience.
By providing students with dynamic and interactive learning experiences, personal tutors can help to enhance student engagement and motivation. Interactive learning activities, such as scenario-based activities, simulations, and games, can provide students with hands-on experience, allowing them to explore complex concepts in a fun and engaging way (Campos, Nogal, Caliz, & Juan, 2020). By incorporating these types of activities into their lessons, personal tutors can help students to develop a deeper understanding of the material, leading to improved academic outcomes. Additionally, by providing students with interactive and engaging learning experiences, personal tutors can help to reduce boredom and increase motivation, leading to a more positive learning experience.
Challenges and Considerations
While the integration of analytics in personal tutoring has many benefits, there are also a number of challenges that must be considered. Remember, every exciting journey has its challenges, and ours is no different. One of the main challenges is the technology required to bring this vision to life. Tutors must have access to data analytics tools and software that can provide collect, analyse, store and disseminate meaningful insights into student performance. This requires universities to invest in technology and infrastructure, as well as to provide training for personal tutors to use these tools effectively. Recently, some virtual learning environments (VLEs) incorporate analytics in their setup, for example, Canvas (those using Canvas may be familiar with Figure 3). And it doesn't stop there. Once the tools are in hand, tutors need to be adept at using them. This introduces yet another challenge – the need for comprehensive training and support. Personal or academic tutors must have the skills and knowledge to use the data analysis tools and to interpret the results. This requires investment in training programs that provide personal tutors with the skills they need to effectively use analytics in their practice.
In order to overcome the challenges associated with integrating analytics into personal tutoring, universities must invest in the necessary technology and training. This includes data analytics tools and software, as well as training programs that provide personal tutors with the skills they need to effectively use analytics in their practice. For example, encouraging the development and incorporation of dashboards within virtual learning environments (VLE) to enable automated analytics. Personal tutors must also be provided with support in using these tools, including ongoing training and professional development opportunities. This can be achieved through regular workshops and training sessions, as well as through access to online resources and support networks.
Despite the challenges discussed above, the integration of analytics into personal tutoring is crucial for the benefit of students. Overcoming the challenges associated with integrating analytics into personal tutoring is essential for improving the outcomes for students. By investing in the necessary technology and training, universities can ensure that personal tutors have the skills and knowledge they need to effectively use analytics in their practice. This can lead to improved retention rates, increased graduation rates, and higher grades for students.
Conclusion
The use of analytics in personal tutoring offers numerous benefits for both students and tutors. By leveraging the power of data and analysis, personal tutors can provide students with the support they need to succeed, leading to improved retention rates, increased graduation rates, and higher grades. However, integrating analytics into personal tutoring also presents some challenges, including the necessary technology and training required to support this integration. It is clear that universities should invest in the necessary technology and training to support the integration of analytics into personal tutoring. This investment will not only help to ensure that personal tutors are equipped with the tools they need to succeed but also create opportunities for students to receive personalised and effective support.
In light of this discussion, I would love to hear your thoughts and opinions on the role of analytics in personal tutoring. Do you believe that analytics has the potential to transform the way personal tutors support students? What challenges do you see with the integration of analytics into personal tutoring? How can these challenges be overcome to increase the widespread adoption of analytics in education. Let's continue this discussion in the comments and work together to create more effective and personalised support systems for our students.
References
Arroyo, I., Woolf, B. P., Burelson, W., Muldner, K., Rai, D., & Tai, M. (2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. International Journal of Artificial Intelligence in Education, 24, 387–426.
Campos, N., Nogal, M., Caliz, C., & Juan, A. A. (2020). Simulation-based education involving online and on-campus models in different European universities. International Journal of Educational Technology in Higher Education, 17, 1–15.
Dichev, C., & Dicheva, D. (2017). Gamifying education: what is known, what is believed and what remains uncertain: a critical review. International Journal of Educational Technology in Higher Education, 14(1), 1–36.
Evans, J. R., & Lindner, C. H. (2012). Business Analytics: The Next Frontier for Decision Sciences. Decision Line, 43(2), 4–6. Retrieved from https://doi.org/10.1007/978-1-4614-6080-0
Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C. A., & Ramírez Mendoza, R. A. (2022). Learning analytics: state of the art. International Journal on Interactive Design and Manufacturing (IJIDeM), 16(3), 1209–1230.
Karumbaiah, S., Baker, R., Tao, Y., & Liu, Z. (2022). How does students’ affect in virtual learning relate to their outcomes? A systematic review challenging the positive-negative dichotomy. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 24–33).
Koksal, I. (2020). The Rise Of Online Learning. Retrieved 9 February 2023, from https://www.forbes.com/sites/ilkerkoksal/2020/05/02/the-rise-of-online-learning/
Lowes, R. (2020). Knowing you: Personal tutoring, learning analytics and the Johari Window. In Frontiers in Education (Vol. 5, p. 101). Frontiers Media SA.
Pulker, H. (2019). Learning Analytics to improve retention. Distances et Médiations Des Savoirs, 28(28). Retrieved from https://doi.org/10.4000/DMS.4602
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Roldán-Merino, J., Miguel-Ruiz, D., Roca-Capara, N., & Rodrigo-Pedrosa, O. (2019). Personal tutoring in nursing studies: A supportive relationship experience aimed at integrating, curricular theory and professional practice. Nurse Education in Practice, 37, 81–87.
Shemshack, A., & Spector, J. M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments, 7(1), 1–20.
US Department of Education. (2017). Issue Brief: Academic Tutoring in High Schools. Retrieved 9 February 2023, from https://www2.ed.gov/rschstat/eval/high-school/academic-tutoring.pdf
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110.
Wakelin, E. (2022). Personal Tutoring in Higher Education: an action research project on how improve personal tutoring for both staff and students. Educational Action Research, 1–16.
Zhang, L., Basham, J. D., & Yang, S. (2020). Understanding the implementation of personalized learning: A research synthesis. Educational Research Review, 31, 100339.
For more information on this topic, watch the recording of Aniekan's UKAT webinar from September 2023.
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