The Graduate Programs Office has recently restructured and has developed tools, personnel, and strategies to increase retention, persistence, and graduation among students in the business school’s graduate programs. A significant part of this restructuring has been to professionalize advising services to its students by providing quality, personal advising to each graduate student. This level of focused, professional advising is critical in ensuring the best possible outcomes for students (Jones 2018). One of the ways to measure the effectiveness of these advising strategies is to survey students about their level of satisfaction with their advising experience. Such a survey was conducted of recent graduates from 2021-2022. The results of that survey are analysed herein to determine how effective the advising has been, to assess overall satisfaction with the advising experiences, and to understand student sentiment and suggestions for improving the process of advising going forward.
Project Setup
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Note: All identifiable student and advisor information contained in the original survey data was removed to protect individual privacy. The data uploaded for this analysis does not contain personal information such as student name or identification number.
Analysis Questions
Question 1: What type of interaction(s) did students have with their advisor?
Question 2: What was the overall level of satisfaction with the advising experience and how likely are students to recommend the program to others?
Question 3: Why are students likely to recommend this program based on their advising experience?
Analysis of Question 1
Question 1: What type of interaction(s) did students have with their advisor?
Students have various needs when it comes to advising, and one of the most important steps in building a relationship with students is to welcome them to the program and answer their initial questions. Other types of appointments indicate a level of engagement with students including making changes to their degree plan, academic advising, and engagement with campus resources. An additional component of a professional advisor’s day-to-day work is to help with recruiting new students and helping to remove barriers to during the admissions process.
In this survey, there were 225 responses. While this does not fully capture the day-to-day interactions with all students, it does provide a snapshot of those engagement opportunities.
Appt_type <- Advisor_Statisfaction_Survey$"Appointment Type"Appointment_list <-strsplit(Appt_type, ", ", fixed =TRUE)Appointment_count <-table(unlist(Appointment_list))Appointment_counts_df <-as.data.frame(Appointment_count)names(Appointment_counts_df) <-c("Appointment", "Count")library(ggplot2)ggplot(Appointment_counts_df, aes(x = Appointment, y = Count, fill = Appointment)) +geom_bar(stat ="identity") +labs(title ="Survey Results by Appointment Type", x ="Appointment", y ="Count") +theme(axis.text.x =element_text(angle =10, hjust = .5)) +scale_fill_manual(values =c("Welcome Call"="skyblue", "Advising"="orange", "Email"="green", "Prospective Student"="red", "Other"="blue"))
Analysis - From this chart, we can see that students most frequently engage with advisors for advising. This interaction could be as simple as asking a question about a class or asking to take a different course in a specific semester. The Welcome Call is significant as a proportion of appointments for students. This analysis is consistent with research that discusses the importance of forming relationships with students throughout their course of study and the impact on their participation in and response to alumni surveys [McDonald and Woodard (2021)](Drummond Hays 2000).
Analysis of Question 2
Question 2: What was the overall level of satisfaction with the advising experience and how likely are students to recommend the program to others?
This question was asked of students regarding their overall level of satisfaction with the advising experience and with their individual interactions with their assigned advisor. This satisfaction level was measured using the Likert Scale
extremely_satisfied <- Advisor_Statisfaction_Survey$'Satisfaction_Level'extremely_satisfied <-as.character(extremely_satisfied)satisfaction_list <-strsplit(extremely_satisfied, ", ", fixed =TRUE)satisfaction_levels <-c("Extremely satisfied", "Somewhat satisfied", "Neither satisfied nor dissatisfied", "Somewhat dissatisfied", "Extremely dissatisfied")satisfaction_counts <-table(unlist(satisfaction_list))satisfaction_counts_df <-as.data.frame(satisfaction_counts)names(satisfaction_counts_df) <-c("Satisfaction", "Count")library(ggplot2)ggplot(satisfaction_counts_df, aes(x = Satisfaction, y = Count, fill = Satisfaction)) +geom_bar(stat ="identity") +labs(title ="Survey Results by Level of Satisfaction", x ="Satisfaction", y ="Count") +theme(axis.text.x =element_text(angle =0, hjust =-5)) +scale_fill_manual(values =c("Extremely satisfied"="skyblue","Somewhat satisfied"="orange","Neither satisfied nor dissatisfied"="green","Somewhat dissatisfied"="red","Extremely dissatisfied"="purple"))
As clearly depicted in this chart, students are overwhelmingly “extremely satisfied” with the advising experience. However, one measure of satisfaction, is the student’s willingness, or eagerness, to recommend the program or service to others. In this case, the survey also asked that question as well.
Will_Recommend <- Advisor_Statisfaction_Survey$Will_Recommendlibrary(dplyr)recommend_counts <-table(Will_Recommend)recommend_counts_df <-as.data.frame(recommend_counts)names(recommend_counts_df) <-c("Rating", "Frequency")library(ggplot2)ggplot(recommend_counts_df, aes(x = Rating, y = Frequency)) +geom_bar(stat ="identity", fill ="skyblue") +labs(title ="Distribution of Recommendation Ratings", x ="Rating", y ="Frequency") +theme_minimal()
From this chart, we can see a distinct parallel between the overwhelming level of student satisfaction and the likelihood that the student will recommend the program to others. This finding is consistent with research on customer satisfaction ratings and their willingness to recommend a product or service (김현철 and Choi Byung Hoon 2014; Franky and Yanuar Rahmat Syah 2023; 서영수 and Seung Sin Lee 2014). In this case, we can see that students who are pleased with their experience in their learning environment are both extremely satisfied with that experience as well as being overwhelmingly willing to recommend the program to prospective students which is an important part of the recruiting process (Dennis 2020).
Analysis of Question 3
Question 3: Why are students likely to recommend this program based on their advising experience?
To analyze why students are likely to recommend our programs to others, we analyzed the open-form responses to the question that followed “How likely are you to recommend this program?” which was “Please explain why you provided this rating”. First a sentiment analysis using AFINN was conducted, then visualized using a wordcloud.
From this visualization of the student sentiment, we can ascertain that students viewed their experiences with their advisor as being primarily helpful with other positively associated words supporting that notion.
Conclusions
Overall, the analysis of student survey responses shows that students are extraordinarily satisfied with their experiences in our programs. Their experiences interacting with advisors has proven helpful in increasing student engagement, retention, persistence, and completion.
References
Dennis, Marguerite J. 2020. “The Role of Alumni in International Recruiting.”Recruiting & Retaining Adult Learners 23 (3): 1–4. https://doi.org/10.1002/nsr.30670.
Drummond Hays, Sarah. 2000. “Facilitating Master’s Student Success: A Quantitative Examination of Student Perspectives on Advising.”https://doi.org/10.15760/etd.1502.
Franky, Franky, and Tantri Yanuar Rahmat Syah. 2023. “The Effect of Customer Experience, Customer Satisfaction, and Customer Loyalty on Brand Power and Willingness to Pay a Price Premium.”Quantitative Economics and Management Studies 4 (3): 437–52. https://doi.org/10.35877/454ri.qems1639.
Jones, Kyle M. L. 2018. “Advising the Whole Student: eAdvising Analytics and the Contextual Suppression of Advisor Values.”Education and Information Technologies 24 (1): 437–58. https://doi.org/10.1007/s10639-018-9781-8.
McDonald, Courtney, and Tracey L Woodard. 2021. “Criminal Justice Student and Alumni Perspectives on Advising.”Journal of Criminal Justice Education 32 (2): 186–200. https://doi.org/10.1080/10511253.2021.1889629.
김현철, and Choi Byung Hoon. 2014. “Empirical Studies on How Service Quality of RIPC Affects Customer Satisfaction, Revisit Intention and Recommend Intention.”Public Policy Review 28 (3): 349–77. https://doi.org/10.17327/IPPA.2014.28.3.014.
서영수, and Seung Sin Lee. 2014. “A Study on Consumer Satisfaction and Willingness to Recommend by the Innovation Diffusion Theory: Comparison on Different Technology Adoption Stages of Smartphone.”Journal of Consumption Culture 17 (1): 89–111. https://doi.org/10.17053/JCC.2014.17.1.005.
