User Evaluation of the Master of Applied Statistics Program Curriculum

Introduction

In today’s rapidly evolving educational landscape, the continuous evaluation and improvement of academic programs are paramount. The Master of Applied Statistics program must be regularly assessed based on user feedback to ensure its relevance and effectiveness. Evaluating this program’s curriculum from the perspective of students, alumni, employers, and other stakeholders is crucial for enhancing program quality, meeting stakeholder expectations, and ensuring long-term success.

Enhancing Program Quality

Regular curriculum evaluations help identify strengths and areas for improvement. Feedback from students and alumni provides insights into course effectiveness, relevance of material, and overall learning experience. This allows the program to evolve, remaining rigorous and pertinent.

Meeting Stakeholder Expectations

Educational programs must align with industry standards and expectations. Employers can offer valuable input on practical skills and knowledge required in the workplace. Incorporating this feedback ensures graduates are well-prepared for their careers, enhancing their employability and the program’s reputation.

Ensuring Long-term Success

The higher education landscape is dynamic. Regular evaluations based on user feedback enable the program to adapt to new developments and trends, such as advancements in technology and data science. This proactive approach ensures graduates are equipped to tackle contemporary challenges and excel in their careers.

Data Analysis

1. Alumni Instituion

data$Institution<-factor(data$Nama.perusahaan.instansi)
data$Institution<-recode(data$Institution,
"BPS"="Central Bureau of Statistics",
"Badan Pusat Statistik"="Central Bureau of Statistics") 


Institution = tabyl(data, Institution) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 2)
names(Institution) = c("Institution", "Frequency", "Percentage")



Institution %>%
  flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
  vline() %>% vline_left() %>% align(align="center",part="all") %>%
  add_header_row(values="Table 1: Distribution of Institution", colwidths=3) %>%
  align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")

Table 1: Distribution of Institution
Institution	Frequency	Percentage
Central Bureau of Statistics	4	50.00%
bank bjb	1	12.50%
DJP	1	12.50%
LK3P UI	1	12.50%
TEEG Indonesia	1	12.50%
Total	8	100.00%

Institution_plot <- ggplot(Institution[-nrow(Institution),], aes(x = as.factor(Institution), y = Frequency, fill = as.factor(Institution))) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = Frequency), vjust = -0.5) + # Add labels above the bars
  xlab("Institution") +
  ylab("Frequency") +guides(fill="none", color="none")+
  ggtitle("Figure 2. Alumni Institutions") + theme(legend.Institution = "none")+theme_bw()+
  theme(plot.title = element_text(size = 15, face = "bold"))+scale_x_discrete(guide = guide_axis(angle = 90))

Institution_plot

Based on Figure 1, the majority of BPS respondents participated in the user satisfaction survey. This aligns with our observation that many of our students over the years have come from BPS.

2. Technical Competence of Graduates from the Master’s Program in Statistics

data$Competency<-factor(data$Bagaimana.penilaian.Anda.terhadap.kompetensi.teknis.lulusan.Program.S2.Statistika.)

data$Competency<-recode(data$Competency,
"Sangat Baik"="Excellence",
"Baik"="Good",
"Cukup Baik"="Average",
"Kurang Baik"="Poor")
 
  
Competency = tabyl(data, Competency) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 2)
names(Competency) = c("Competency", "Frequency", "Percentage")


Competency %>%
  flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
  vline() %>% vline_left() %>% align(align="center",part="all") %>%
  add_header_row(values="Table 2: Technical Competence of Graduates from the Master's Program in Statistics", colwidths=3) %>%
  align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")

Table 2: Technical Competence of Graduates from the Master's Program in Statistics
Competency	Frequency	Percentage
Good	4	50.00%
Excellence	4	50.00%
Total	8	100.00%

# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Competency<- plot_ly(Competency[-nrow(Competency),], labels = ~Competency, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
  layout(title = 'Figure 2: Technical Competence')

pie_Competency

3. Graduates able to apply statistical knowledge in their daily work.

data$Relevance<-factor(data$Seberapa.baik.lulusan.mampu.menerapkan.ilmu.statistika.dalam.pekerjaan.sehari.hari.)

data$Relevance<-recode(data$Relevance,
"Sangat Baik"="Excellence",
"Baik"="Good",
"Cukup Baik"="Average",
"Kurang Baik"="Poor")
 
  
Relevance = tabyl(data, Relevance) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 2)
names(Relevance) = c("Relevance", "Frequency", "Percentage")


Relevance %>%
  flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
  vline() %>% vline_left() %>% align(align="center",part="all") %>%
  add_header_row(values="Table 3: Relevance of the Statistics Curriculum to the Professional Workforce", colwidths=3) %>%
  align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")

Table 3: Relevance of the Statistics Curriculum to the Professional Workforce
Relevance	Frequency	Percentage
Good	5	62.50%
Excellence	3	37.50%
Total	8	100.00%

# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Relevance<- plot_ly(Relevance[-nrow(Relevance),], labels = ~Relevance, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
  layout(title = 'Figure 3: Relevance of the Statistics Curriculum ')

pie_Relevance

4. Communication Skills

data$Communication<-factor(data$Bagaimana.penilaian.Anda.terhadap.kemampuan.komunikasi.lulusan.)

data$Communication<-recode(data$Communication,
"Sangat Baik"="Excellence",
"Baik"="Good",
"Cukup Baik"="Average",
"Kurang Baik"="Poor")
 
  
Communication = tabyl(data, Communication) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 2)
names(Communication) = c("Communication", "Frequency", "Percentage")


Communication %>%
  flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
  vline() %>% vline_left() %>% align(align="center",part="all") %>%
  add_header_row(values="Table 4: Cummunication Skills of Alumni", colwidths=3) %>%
  align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")

Table 4: Cummunication Skills of Alumni
Communication	Frequency	Percentage
Good	4	50.00%
Excellence	4	50.00%
Total	8	100.00%

# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Communication<- plot_ly(Communication[-nrow(Communication),], labels = ~Communication, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
  layout(title = 'Figure 4: Cummunication Skills of Alumni')

pie_Communication

5. Collaboration Skills

data$Collaboration<-factor(data$Bagaimana.penilaian.Anda.terhadap.kemampuan.kerjasama.tim.lulusan.)

data$Collaboration<-recode(data$Collaboration,
"Sangat Baik"="Excellence",
"Baik"="Good",
"Cukup Baik"="Average",
"Kurang Baik"="Poor")
 
  
Collaboration = tabyl(data, Collaboration) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 2)
names(Collaboration) = c("Collaboration", "Frequency", "Percentage")


Collaboration %>%
  flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
  vline() %>% vline_left() %>% align(align="center",part="all") %>%
  add_header_row(values="Table 5: Cummunication Skills of Alumni", colwidths=3) %>%
  align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")

Table 5: Cummunication Skills of Alumni
Collaboration	Frequency	Percentage
Good	1	12.50%
Excellence	7	87.50%
Total	8	100.00%

# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Collaboration<- plot_ly(Collaboration[-nrow(Collaboration),], labels = ~Collaboration, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
  layout(title = 'Figure 5: Cummunication Skills of Alumni')

pie_Collaboration

6. Graduates possess knowledge and skills in utilizing big data, artificial intelligence (AI), and the Internet of Things (IoT).

data$Updating<-factor(data$Apakah.lulusan.memiliki.pengetahuan.dan.keterampilan.dalam.penggunaan.big.data..artificial.intelligence..AI...dan.Internet.of.Things..IoT..)

data$Updating<-recode(data$Updating,
"Sangat Baik"="Excellence",
"Baik"="Good",
"Cukup Baik"="Average",
"Kurang Baik"="Poor")
 
  
Updating = tabyl(data, Updating) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 2)
names(Updating) = c("Updating", "Frequency", "Percentage")


Updating %>%
  flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
  vline() %>% vline_left() %>% align(align="center",part="all") %>%
  add_header_row(values="Table 6: Graduates possess knowledge and skills in utilizing big data, artificial intelligence (AI), and the Internet of Things (IoT)", colwidths=3) %>%
  align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")

Table 6: Graduates possess knowledge and skills in utilizing big data, artificial intelligence (AI), and the Internet of Things (IoT)
Updating	Frequency	Percentage
Good	3	37.50%
Average	2	25.00%
Excellence	3	37.50%
Total	8	100.00%

# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Updating<- plot_ly(Updating[-nrow(Updating),], labels = ~Updating, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
  layout(title = 'Figure 6: Graduates possess knowledge and skills in Society 5.0')

pie_Updating

7. Adaptation Skills

data$Adaptation<-factor(data$Bagaimana.kemampuan.lulusan.untuk.beradaptasi.dengan.perkembangan.teknologi.di.era.Society.5.0.)

data$Adaptation<-recode(data$Adaptation,
"Sangat Baik"="Excellence",
"Baik"="Good",
"Cukup Baik"="Average",
"Kurang Baik"="Poor")
 
  
Adaptation = tabyl(data, Adaptation) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 2)
names(Adaptation) = c("Adaptation", "Frequency", "Percentage")


Adaptation %>%
  flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
  vline() %>% vline_left() %>% align(align="center",part="all") %>%
  add_header_row(values="Table 7: Cummunication Skills of Alumni on Society 5.0", colwidths=3) %>%
  align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")

Table 7: Cummunication Skills of Alumni on Society 5.0
Adaptation	Frequency	Percentage
Good	4	50.00%
Average	1	12.50%
Excellence	3	37.50%
Total	8	100.00%

# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Adaptation<- plot_ly(Adaptation[-nrow(Adaptation),], labels = ~Adaptation, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
  layout(title = 'Figure 7: Cummunication Skills of Alumni on Society 5.0')

pie_Adaptation

Suggestions for new courses that are relevant to the Society 5.0 era

text <-data$Apakah.ada.kompetensi.lain.yang.menurut.Anda.perlu.dimiliki.oleh.lulusan.Program.S2 
text = unlist(strsplit(text, "\\W+"))
text = tolower(text)
text = data.frame(table(text))

#wordcloud(text$text, text$Freq)

text = text[!is.element(text$text, stopw),]

wordcloud(text$text, text$Freq, min.freq = 1, random.order=FALSE, rot.per=0.25, colors=brewer.pal(15, "Dark2"))

## Warning in brewer.pal(15, "Dark2"): n too large, allowed maximum for palette Dark2 is 8
## Returning the palette you asked for with that many colors

Suggestions for Continuous Curriculum Improvement

# Create the data frame with the provided text data
text <-data$Apakah.ada.saran.atau.rekomendasi.terkait.perbaikan.atau.penambahan.mata.kuliah.yang.sesuai.dengan.kebutuhan.industri.dan.perkembangan.teknologi. 
text = unlist(strsplit(text, "\\W+"))
text = tolower(text)
text = data.frame(table(text))
text = text[!is.element(text$text, stopw),]
wordcloud(text$text, text$Freq, min.freq = 1, random.order=FALSE, rot.per=0.25, colors=brewer.pal(15, "Dark2"))

## Warning in brewer.pal(15, "Dark2"): n too large, allowed maximum for palette Dark2 is 8
## Returning the palette you asked for with that many colors

8. Graduate User Satisfaction

data$Satisfaction<-factor(data$Seberapa.puas.Anda.dengan.kinerja.lulusan.Program.S2.Statistika.di.perusahaan.instansi.Anda.)

data$Satisfaction<-recode(data$Satisfaction,
"Sangat Puas"="Excellence",
"Puas"="Good",
"Cukup Puas"="Average",
"Kurang Puas"="Poor")
 
  
Satisfaction = tabyl(data, Satisfaction) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 2)
names(Satisfaction) = c("Satisfaction", "Frequency", "Percentage")


Satisfaction %>%
  flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
  vline() %>% vline_left() %>% align(align="center",part="all") %>%
  add_header_row(values="Table 8: Graduate User Satisfaction", colwidths=3) %>%
  align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")

Table 8: Graduate User Satisfaction
Satisfaction	Frequency	Percentage
Good	5	62.50%
Excellence	3	37.50%
Total	8	100.00%

# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Satisfaction<- plot_ly(Satisfaction[-nrow(Satisfaction),], labels = ~Satisfaction, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
  layout(title = 'Figure 8: Graduate User Satisfaction')

pie_Satisfaction

# Create the data frame with the provided text data
text <-data$Apakah.ada.hal.lain.yang.ingin.Anda.sampaikan.terkait.kurikulum.Program.S2.Statistika.
  
text = unlist(strsplit(text, "\\W+"))
text = tolower(text)
text = data.frame(table(text))
text = text[!is.element(text$text, stopw),]
wordcloud(text$text, text$Freq, min.freq = 1, random.order=FALSE, rot.per=0.25, colors=brewer.pal(15, "Dark2"))

8. Graduate User Recomendation

data$Recomendation<-factor(data$Seberapa.besar.kemungkinan.Anda.merekomendasikan.lulusan.Program.S2.Statistika.kepada.perusahaan.instansi.lain.)

data$Recomendation<-recode(data$Recomendation,
"Sangat Besar"="Excellence",
"Besar"="Good",
"Cukup Besar"="Average",
"Kurang Besar"="Poor")
 
  
Recomendation = tabyl(data, Recomendation) %>% 
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 2)
names(Recomendation) = c("Recomendation", "Frequency", "Percentage")


Recomendation %>%
  flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
  vline() %>% vline_left() %>% align(align="center",part="all") %>%
  add_header_row(values="Table 9: Possibility of recommending alumni of applied statistics master's degree to other institutions", colwidths=3) %>%
  align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")

Table 9: Possibility of recommending alumni of applied statistics master's degree to other institutions
Recomendation	Frequency	Percentage
Good	4	50.00%
Average	1	12.50%
Excellence	3	37.50%
Total	8	100.00%

pie_Recomendation<- plot_ly(Recomendation[-nrow(Recomendation),], labels = ~Recomendation, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
  layout(title = 'Figure 9: Possibility of recommending alumni to other institutions')

pie_Recomendation

Suggestions for updating curriculum

# Create the data frame with the provided text data
text <-data$Apakah.ada.hal.lain.yang.ingin.Anda.sampaikan.terkait.kurikulum.Program.S2.Statistika.Terapan.FMIPA.UNPAD.
  
text = unlist(strsplit(text, "\\W+"))
text = tolower(text)
text = data.frame(table(text))
text = text[!is.element(text$text, stopw),]
wordcloud(text$text, text$Freq, min.freq = 1, random.order=FALSE, rot.per=0.25, colors=brewer.pal(15, "Dark2"))

Conclusion

Overall, graduates of the applied statistics master’s program are highly regarded by employers, who express strong satisfaction with their performance. However, users have suggested incorporating courses on Artificial Intelligence to align with the latest advancements in AI technology. Evaluating the Master of Applied Statistics program’s curriculum based on user feedback is essential for several reasons. It helps enhance program quality, meet stakeholder expectations, and ensure long-term success. By fostering a culture of continuous improvement and adapting to the evolving educational landscape, the program can maintain its relevance and effectiveness. Ultimately, satisfying stakeholders’ needs and expectations is crucial for the program’s sustainability and its ability to produce competent, industry-ready graduates.