Sustainable Enhancement of Statistical Literacy: Utilizing Data Analysis and Text Mining to Identify Statistical Issues

1. Introduction

The development of an information society, the growth of technology and the need to face, interpretate and analyze information from different sources, makes knowledge of statistics essential in making and communicating decisions, both in private life and professionally [1]. This knowledge of statistics, which may be referred to as the concept of “statistical literacy”, refers to the minimum statistics knowledge required to understand the world [1]. One of the pillars of acquiring “statistical literacy” is education, and therefore, statistics as a subject is present in the majority of university degrees independent of the degree area or discipline. Based on one of the main goals of third-level education, which is to prepare individuals to be able to face the real world, statistical education should focus in the first instance on enriching the base of statistical knowledge and continue promoting its growth through reasoning and statistical thinking and then converting that competence into knowledge that can be put into practice in real-life situations [2].

However, it has been identified that statistics is poorly taught in formal education teaching [3]. Some of the problems detected are that the basic statistical concepts are not understood; integration of statistics as a subject in school is not yet a fact (in certain countries); in order to go through statistics inference, teachers may need to speed up previous explanations, and; also, students have short and restricted time to assimilate the concepts [4]. Other issues highlighted by research include that statistical concepts are treated in isolation without considering the relationship that must be established between them, or the relationship of the statistics concepts with those of other subjects. This manifests itself as situations in the classroom may not sufficiently relate to the professional profile of the students or are not related to real-life experiences [5,6,7].

The lack of accurate knowledge in statistics by students of Social Sciences, Sciences of Behavior or Education, among others, is a recurring topic faced by lecturers and researchers for at least thirty years [2]. Additionally, not only are there theorical issues but also negative emotional reactions, attitudes and beliefs have been reported towards statistics by some students with little interest in the area [8]. Behar y Grima [9] stated that, when talking about the problems in teaching statistics, the following should be considered: high levels of anxiety in students who face this subject, negative attitudes towards it, the content of the course (excessively focused on probability and mathematics), lack of motivation in students, frustration by some teachers for not seeing results from their efforts and the influence of the technology on the teaching methods. Subsequently, statistics teaching has become a broad field of research and development [10]. Furthermore, Estrella [11] states that this emerging field provides results on how statistics can be taught in the school classrooms, and extending this to higher levels such as university, in order to promote functional, deep and sustainable learning. An educator may improve the effectiveness of the teaching-learning process by adapting the content and material used in the classroom. In this context, the negative attitude of the student facing statistics and the adaption of the teaching-learning process, require the identification of what are the main statistical issues faced by students, e.g., meaning of statistics, goal of statistics, basic concepts, statistic formulas, probability, etc.

In this study, in order to identify students’ statistical issues, a sustainable approach is taken whereby data is drawn from two international universities (University College Dublin (UCD) in Ireland and University Rey Juan Carlos (URJC) from Spain). These universities involve differences in cultures, in the ways of facing education, and in general, different point of views related to statistics and University education. UCD is one of Europe’s leading research-intensive universities where undergraduate education, masters and Phd training, research, innovation and community engagement form a dynamic spectrum of activity. UCD is also Ireland’s most globally engaged university with over 38,000 students drawn from 152 countries, including over 5,000 students based at locations outside of Ireland. The UCD data is sourced from the UCD Maths Support Centre (MSC). This opened in 2004 and is a free tutoring support service for all UCD students studying a mathematics or statistics related modules. University Rey Juan Carlos (URJC) is the second-biggest public university in the community of Madrid. It offers a wide range of degrees including 88 Bachelor’s degrees, 9 English-degrees, 9 Bachelor’s degrees online and 75 dual degrees, as well as a wide range of official master’s degrees and Phd programs. Bachelor’s degrees are organized in five knowledge areas (Social Sciences and Law, Sciences, Health Sciences, Engineering and Architecture and Art & Humanities) with statistics modules present across the five areas. Ottaviani [12] suggests that statistics research cannot be done in isolation and interdisciplinary work needs to be applied, with this being even more beneficial if it is cross border and creates international community. This international approach of examining statistical issues across international borders gives us an added value in time, in money and in effort for future international initiatives, and this collaboration constitutes a pillar for the beginning of a stronger networking between the countries. In financial terms, this collaboration will enhance internal resources’ movement from one country to another. From a tech perspective, this collaboration will improve international cooperation and increase knowledge exchange, including promoting the development of new educational technologies.

To identify what are the statistical topics taught in the entry levels of University, and for international appeal, in this study we analyzed the statistical topics taught across the URJC curriculum and created URJC categories of statistical concepts. In order to identify what are the main statistics problems for students in the transition between secondary level and third level, we have collected, used and analyzed data from the students who have visited the UCD MSC - a free tutoring service for UCD students. When a tutor finishes a session with a student, the tutor records the main statistical topics taught from a drop-down menu (UCD MSC categories of statistics concepts), and they also record an open-text comment on the issues/areas that the student struggled with. This categorization process in the UCD Math Support is a manual activity completed by tutors. The categorization for statistical issues has not been validated before, and therefore, it could have errors. For example, tutors may not have enough information about the student problem to identify the category, categories are subjective as tutors may consider different categories for the same student issue, and tutors may be under time pressure and select a random category. Also, there is a standard category called Other and it may be used in cases where the tutor does not know what category to assign the issue to. We manually categorized the open-text comments according to the defined URJC categories and then conducted a comment-specific evaluation of the statistical challenges to determine which issues are common across multiple modules/Universities (UCD and URJC). We have conducted a sustainable descriptive analysis to identify statistical topics students struggle with internationally. Additionally, we have applied eco-friendly text mining to the open-ended tutor comments to identify the statistical topics that students struggle with. Building on this study, we plan to develop a suite of sustainable educational applications and games to support undergraduate students in first-and second-year statistical modules. This analysis will contribute to the cultivation of lasting statistical literacy among students. Combining the structure of the MSC data and the URJC categories, we address the following research questions:

1)

What are the recurring statistical issues posing challenges to students?

2)

What are the similarities between UCD MSC categories and URJC categories?

3)

How can text mining extract insights from open-text responses to uncover specific statistical difficulties?

2. Materials and Methods

In this section we go through the methodology used to develop the URJC categories, explain how the UCD MSC works and how its data are collected, how the MSC tutor comments were classified under the URJC categories, and the techniques applied to analyze the data.

2.1. Methodology for Categorization of URJC Modules

With the goal of categorization of statistical issues, before working with the UCD Math Support Centre data, every statistics module description webpage for every degree taught in the URJC were collected and reviewed. Next the statistics concepts were extracted and analyzed in order to create the URJC categories of statistical issues. Our methodology to create the categories is composed of the following steps:

• Accessed the URJC website and identified every School:
  • Social Sciences and Law
  • Sciences
  • Health Sciences
  • Engineering and Architecture
  • Art & Humanities
• Identified every degree inside every School in which a statistics module is taught.
• Downloaded every module webpage for every statistics module and identified the statistical Lessons for each module.
• Summarized, compared and matched the statistical Lessons across the modules.
• Checked what Lessons were consistent across different modules and identified what are the main Lessons that statistic covers.
• Created a description for each Lesson.

From this review process, we define three Topics and five Lessons (see Table 1). These will be referred to as the URJC categories and represent the main categorization used in this study.

2.2. Description of UCD MSC data

The main data set for this study is the data from UCD students who have accessed the UCD MSC since 2015/16 for statistics support and have consented for their data to be used for research purposes. The ethics application was approved by the UCD Office of Research Ethics. Between January 2015 and September 2023, there have been approximately 32,000 visits to the MSC. The MSC provides support for various mathematics related subjects (e.g., mathematics, applied and computational mathematics, physics, and economics), and statistics visitors are the second largest cohort of visitors.

The UCD MSC uses a session management system to electronically record details of a student’s visit to the MSC, for example, date of visit and module code that the student is visiting in reference to. Using the system, each MSC tutor can categorize the student’s presenting issue from a drop-down list and record an open-text response for the session. In the open-text response, tutors are asked to identify the mathematical problem the student requested help with and describe how they helped the student. In addition, since CoVid-19, students can provide an open-text response describing the issue they are requesting help with when booking a MSC session (if they choose to book an appointment rather than drop into the MSC without booking). The following video link shows how the UCD MSC system works and how a student takes part in it: https://youtu.be/pbswG50OuCs.

For each MSC visit for statistics support, the following data were provided (see Table 2 for examples):

1) Code: the module code that the student sought help for,

2) Name and Description: the presenting issue as categorized by the tutor for the session,

3) Time: time stamp of student’s entry to the MSC,

4) Comment: the tutor’s open-text response outlining the statistical issue that the student needed help with and how they helped the student, or, in some cases it could also include an open-text response by the student describing the statistical issue they are requesting help with. Students’ comments are indicated by quotation marks, for example, Student Query: “sampling distributions”.

2.3. Classification of Tutor Comments in the UCD MSC Data

The data for the UCD MSC consisted of 3,570 data entries collected from student visits to the MSC from January 2015 to September 2023. A key feature of the data are the tutors’ comments (see Comment variable in Table 2) because these provide a rich source of information on statistical issues. As Topics and Lessons were already defined, the next step was to match the tutor comments with the Lessons. This was a manually process which started with a sample of 49 unique comments for which the first author and third author analyzed separately according to the URJC categories. After that, the categorization between the authors was cross tabulated in order to validate it. There was a high percentage of similarities in the comparison, with the same result in 48 out of 49 comments in total (an agreement of 95%). Disagreements were discussed and classifications agreed upon. Subsequently, the first author and third author continued doing the manual coding, with the first author manually coding the majority of cases. As coding of the comments progressed, and through discussions between the authors, it became clear that additional Topics and Lessons beyond the initial five Lessons were needed. Therefore, more Lessons were added. The first new category was “High Level”; this was added because of comments related to Topics which were not considered related to introductory or service level statistics. The second new Lesson related to Statistics Software problems because there were many students with problems in software. Last but not least, was a new category called Other. This was for students whose problems were not related with statistics or there was not enough information to determine Lesson categorization. Categorization of the comments were examined a second time by the first author to ensure consistency.

Once the initial categorization of the UCD MSC tutors’ comments based on the URJC classification was completed, several issues were identified. Students whose comments matched with two or more Lessons (n=73) were split into as many Lessons as they had matched. This strategy is based on the main goal of the paper: to identify the statistical issues that students present with as opposed to the number of students visits with statistical issues. An important consideration here is that the data set does not include any identifying features, i.e., student name and number, and students can go into the UCD MSC once, twice or many more times. As a result, the same student could have multiple entries in the data set, so we consider data entries (visits) instead of the number of students. The strategy mentioned above converted n=3,570 data entries to n=3,674 (n being the number of statistical issues).

Another pre-processing task was to add two news categories:

• Module II: for those students who have issues in Lesson 3 and Lesson 4.
• Module I & Module II & Module III: for those students who have issues in all Lessons.

The final URJC categorization is shown in Table 3.

2.4. Statistical Analysis

Once data were collected and Lessons were defined, the next step was to identify statistical issues or Topics that students struggle with. With that goal, data analyses was composed of several data treatments: data wrangling, descriptive analytics, and text mining. RQ1 and RQ2 were addressed using data wrangling, descriptive statistics and graphical analysis. To achieve it, the R ggplot2 package [13] and the R tidyverse package [14] were used.RQ3 was addressed through analyzing the open-response text comments using Natural Language Processing (NLP). Within NPL, word clouds have become one of the most common approaches to analyze text or what is known as text mining. Word clouds are a method to quickly show visualizations of the most common terms based on the frequency of their appearance in the text. Before implementing word clouds, it is necessary to conduct text cleaning and preprocessing. This consisted of using tm package in R [15] to remove prepositions, special characters, numbers, whitespace, convert all the words to lowercase, remove punctuation and remove words that do not give value to this analysis (empty words). The final step before creating the word clouds is to convert the text into a database which allows us to calculate the frequencies of any word in the text.

3. Results

3.1. RQ1: Lesson Data Descriptive Analysis

After the data pre-processing, the next step is to respond to the first research question (RQ1) concerning what Lessons student are struggling with. To achieve this, we conducted an eco-conscious analysis of the Topic categories to identify the recurring statistical themes that pose challenges to students. By applying descriptive statistics to the frequency of these categories, we determined which Lessons present most difficulties for students (see Figure 1).

Figure 1 shows that the largest number of issues are concentrated in Lesson 5: Statistic Inference (n=709) and Lesson 4: Random Variables (n = 609), and the lowest number of issues are in Lesson 6: Statistic Software (n=96). Following the sequential ordering of the Lessons, we can see that the difficulty level goes from the basic to the more advanced level (or complex). In this analysis, the difference between Lesson 1 to Lesson 2 to Lesson 3 does not present as big jumps between each other, but the transition between Lesson 3 to Lesson 4 presents the biggest difference (n= 254). Interestingly, as the level increases, the jumps between the Lessons are also increasing. It is important to consider the numbers of students with issues in Lesson 3 & 4, all of them face problems in Topic II: Probability. If we include them in Lesson 3 and Lesson 4, we increase the number of issues from n=355 to n=459 in Lesson 3 and from n=609 to n=713 in Lesson 4. In that case, it means a big jump in the number of statistical issues from Lesson 2 to Lesson 3 as well, and also, Lesson 4 becomes the main challenge for students. Lesson 6 can be viewed as a subset of the rest of the Lessons because statistics software can be used to implement the statistical methods in any Lesson, so Lesson 6 is not directly related to a level of difficulty.

In our analysis, we focus on the categories related to numbered Lessons. The Other category does not provide information about students’ statistical issues, and High Level is not in our level scope so both are not considered for this research question analysis. Therefore, Figure 2 provides an overview of the proportions only for the Lessons’ categories (without “Other” and “Higher Level”). The percentage of each Lesson in relation to the total is represented to help provide an overview of the proportions. Considering this, we can recognize important statistical issues in Lesson 4 (25.1%) and Lesson 5 (29.2%), and if we merge both of these proportions, it would represent more than half of the statistical issues of our sample.

3.2. RQ2: Similarities between UCD MSC Categories and URJC Categories

Once the URJC categorization was completed, this could be validated by comparing the categorization with the categories (see Name and Description in Table 2) that tutors from UCD made for the same comments (henceforth known as UCD MSC categories). Initial analysis involved analyzing what are the UCD MSC categories and how many times each of them appears. There are 77 categories in total, however, many of them appear only once, for example “Complex Numbers, Vectors and Indices”. Consequently, we consider that the categories which appear less than six times do not have relevance for this validation analysis. Therefore, these categories were deleted, and we have a new subset with 37 categories.

With this preprocessing in UCD MSC categories, there are 9 categories in URJC and in UCD, there are 37 categories so it could witness inconsistency owing to the differences in the number of categories. In fact, the UCD MSC categorization process has not been validated before and it could have errors (see Section 1). The next step undertaken was to merge different UCD MSC categories which could be refer to the same problem (Table 3).

Table 4. Table to show New Categories after merging UCD Categories.

New Merged Category	UCD Categories	N	Merged N
Other	Other	403	1184
	Other (please specify topic)	665
	Word Problem	79
	Student working alone for now	21
	Other (please specify topic):	17
Basic Statistics	Basic Statistics	843
	Standard deviation or variance	44	902
	Graphs (reading, sketching & interpreting)	15
Basic Probability	Basic Probability	196	263
	Basics of Probability theory	67
Random Vectors & Distributions	Continuous distributions (normal, exponential, uniform)	167	325
	Discrete distributions (binomial, poisson, hypergeometric)	82
	Random Vectors	29
	Continuous Probability Distributions:	19
	Properties of Random Samples	16
	Functions	6
	Functions (exponential and logarithmic)	6
Hypothesis Testing & Confidence Intervals	Hypothesis Testing	224
	Confidence Intervals	199
	Hypothesis test – One sample	27
	Hypothesis test - Two samples	19
	Statistical Inference	17	507
	Inference about linear regression	9
	Confidence interval – One sample	6
	Confidence interval – Two samples	6
Linear Regression	Linear Regression	35	35
Integration	Integration	88	88
Arithmetic	Arithmetic	76	76
Statistical Software e.g Minitab, Excel SPSS, R	Statistical Software e.g Minitab, Excel SPSS, R	37	37
Differentation Rules	Differentation Rules	9	9
Differentation	Differentation	7	7
Construction of Estimators	Construction of Estimators	13	13
Basic Algebra	Basic Algebra	8	8
Asymptotics	Asymptotics	7	7
Advanced	Advanced	36	36
Matrices	Matrices	8	8

Table 4. Table to show New Categories after merging UCD Categories.

New Merged Category	UCD Categories	N	Merged N
Other	Other	403	1184
	Other (please specify topic)	665
	Word Problem	79
	Student working alone for now	21
	Other (please specify topic):	17
Basic Statistics	Basic Statistics	843
	Standard deviation or variance	44	902
	Graphs (reading, sketching & interpreting)	15
Basic Probability	Basic Probability	196	263
	Basics of Probability theory	67
Random Vectors & Distributions	Continuous distributions (normal, exponential, uniform)	167	325
	Discrete distributions (binomial, poisson, hypergeometric)	82
	Random Vectors	29
	Continuous Probability Distributions:	19
	Properties of Random Samples	16
	Functions	6
	Functions (exponential and logarithmic)	6
Hypothesis Testing & Confidence Intervals	Hypothesis Testing	224
	Confidence Intervals	199
	Hypothesis test – One sample	27
	Hypothesis test - Two samples	19
	Statistical Inference	17	507
	Inference about linear regression	9
	Confidence interval – One sample	6
	Confidence interval – Two samples	6
Linear Regression	Linear Regression	35	35
Integration	Integration	88	88
Arithmetic	Arithmetic	76	76
Statistical Software e.g Minitab, Excel SPSS, R	Statistical Software e.g Minitab, Excel SPSS, R	37	37
Differentation Rules	Differentation Rules	9	9
Differentation	Differentation	7	7
Construction of Estimators	Construction of Estimators	13	13
Basic Algebra	Basic Algebra	8	8
Asymptotics	Asymptotics	7	7
Advanced	Advanced	36	36
Matrices	Matrices	8	8

After the merging process, there are 17 UCD MSC categories. The frequency or behavior of the UCD MSC categories can be examined (see Figure 3). As a first interpretation and before going deeper into the comparison, Figure 3 shows that the top categories are related with the Topics considered in the URJC categories, for example “Basic Statistics” could be related with Lesson 1; and “Hypothesis Testing & Confidence Intervals” with Lesson 5, and; Random Vectors & Distributions” with Lesson 4. On the other hand, the bottom categories are not clearly related with any URJC Lesson. This could be a good indicator since, based on the URJC descriptions, all of those bottom categories should be matched with the Other category in URJC.

Having cleaned and preprocessed the UCD MSC categories, we apply the comparison between the categorization of URJC with the categorization of UCD. This match allows us to validate both of the categorizations (see Figure 4).

Delving deeper into the comparison between the UCD MSC categories and the URJC categories, the heatmap allows us to identify data density between categories. It shows that there is a moderately strong agreement between all the URJC Lessons (except Lesson 1) with the same topic category for UCD. For example, we can that Lesson 2: Bivariate Data Description matches 46% with Linear Regression and 50% with Matrices and both are part of the Lesson 2 description. Lesson 3: Probability has a 29% match with Basic Probability; Lesson 4: Random Variables & Special Random Variables has a 49% match with “Linear Regression”, Lesson 5: Statistic Inference has a 67% match with Hypothesis Testing & Confidence Intervals. And last but not least, Lesson 6: Statistical Software presents a 46% match with Statistical Software e.g Minitab, Excel, SPSS, R.

Another example is the match of the “Asymptotics” UCD Category. It matches 29% with “High Level” and 43% with “Lesson 5”: Inference Statistics. In Statistics, asymptotic theory is a framework for assessing properties of estimators and statistical test, and extrapolating that to this research, issues relating to estimators are considered as Higher Level, and statistical test issues are considered as part of Lesson 5: Statistic Inference. Hence, as a result, it makes sense to present the percentages for both these relations.

But, on the other side, there are also values in Figure 4 which suggest that some of the issues have not been well categorized. Lesson 5: Inference Statistics has also a moderately strong agreement (46%) with Linear Regression, which, considering the URJC categorization, should be in Lesson 2 only. The MSC category of Basic Statistics presents a similar relation with different URJC Lessons (Lesson 1: 15%, Lesson 2: 12%, Lesson 4: 18% and Lesson 5: 21%). This last point matches a UCD basic concept category with any URJC Lesson level and even, with any URJC Lesson topic.

In summary, we can see that the most common UCD categories have the same topic that the Lesson defined in URJC. Regarding data validation between the UCD and URJC categories, Figure 4 shows that each URCJ Lesson presents its highest percent with a UCD category making sense. Despite it, we can see that some relation needs to be deeper analyzed to understand the clear meaning of the percentage.

3.3. RQ3: Insights from open-text responses

Lessons analysis give us the category where the issue is, but there are several descriptions inside any Lesson. Based on it, we find out in detail what are the main issue descriptions using text mining in the UCD comment text. After pre-processing of the Comment data (see Section 2.4), word clouds were generated for the UCD MSC Comment data, filtered by each URJC Lesson category (see Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10). The word clouds offer a quick snapshot or overview of the most frequently occurring issues cross UCD MSC comments (and across Lessons).

If we go deeper into any word cloud, we can see that in all of them the biggest word has a strong relation with one of the main descriptors of the Lesson. For example, in Lesson 1 word cloud, “standard”, “deviation” and “mean” are the most popular words; in Lesson 2, “regression”, “correlation” and “covariance”; in Lesson 3, “conditional” and “probability”; Lesson 4, “distribution”; Lesson 5, “hypothesis”, “test” and “confidence” and Lesson 6, “query”, “help”, “rstudio”, “code” and “excel”.

Drilling down into the word cloud on Lesson 1 (see Figure 5), provides further detailed information beyond that of the “Description” section in the URJC categorization. The URJC description for Lesson 1 includes: “basic concepts,” “variable types,” “summary of graphics data,” and “summary of numerical data.” The word cloud reveals a finer scale within basic concepts, highlighting terms like “standard deviation,” “mean,” “sample,” and “median.” It also offers more detail about graphical issues, mentioning terms such as “histogram.” Similarly, the other word clouds provide information on students’ statistical issues beyond the URJC category descriptions.

As discussed in the introduction, students struggle not only with theoretical issues but also with emotional ones. Across all word clouds, concepts related to feelings and emotions can be viewed, such as “confused,” “unsure,” and “unclear”.

4. Discussion

The overall goal of this paper is to identify the most common issues students face during the transition from secondary education to tertiary education. At the beginning of this paper, it is presented that statistics modules are included in the majority of degrees independent of degrees area [2], and in some cases that statistics has been poorly taught in formal education teaching [4]. Research has aimed to identify what are the main problems in this teaching statistics. Batanero [5] confirmed that some of the problems detected are understanding the basic statistical concepts, integration of statistics as a subject in school, statistical inference and the limited time to assimilate the concepts [5].

Following this context, our descriptive analysis provides an overview of the frequently occurring issues across URJC Lessons and UCD MSC categories. Addressing RQ1, statistical graphics indicate that probability and statistical inference are areas where students commonly encounter issues. As presented in the introduction, secondary education generally covers basic statistics and descriptive statistics, both related to Lesson 1: Data Description and Lesson 2: Bivariate Data Description. This suggests that students arrive at tertiary education with fewer issues in these areas but struggle more with the new content taught at university level e.g., probability distributions and statistical inference.

One of the key highlights of this study is the establishment of international collaboration in the identification of statistical issues and the associated categorization validation (RQ2). This involved reviewing the taught modules at URJC, categorizing the content into different Lessons, and analyzing the UCD data according to the URJC categories. A heatmap provided deeper insights into the relationship between the URJC Lessons and UCD MSC categories. Overall, there is a moderately strong agreement between the two categorizations. On a more granular level, several interpretations can be drawn from the heatmap, such as the case with the UCD MSC category “Basic Statistics.” The highest percentage of relation is 21% with Lesson 5. However, this category is related to all URJC Lessons, which might indicate that the tutor’s categorization is subjective, based on what they consider basic statistics, or that they are uncertain about the appropriate category. We recommend that the UCD MSC categories are further defined and discussed with the MSC tutors. In addition, the number of categories should be reduced as 40 categories appeared less than 6 times across the 7-year time period of the data.

On a more detailed level, to help address RQ3, word clouds helped identify specific problems within each Lesson. For each word cloud, the most representative word is the main descriptor inside each lesson and, as a first picture and overview, it has positive relation with the categorization. This comprehensive analysis provides an evidence base for developing statistical support for first- and second-year undergraduate students. To develop educational games or apps that benefit students, this analysis helps identify the main statistical Topics taught and the areas where students struggle.

An important gap is the relation between how to teach statistics, the content in the statistics subject and the knowledge of the lecturers. If quality education is not provided, there will be inequality in students’ knowledge level, and they may have negative emotions facing this subject. In addition, students will learn statistics just to pass their examinations, and afterwards, they will forget their module knowledge. Hence, they will be unprepared for real life. This can be addressed through strong networking, collaboration and knowledge sharing of statistics education approaches across universities.

As the future supports (e.g., games, dashboards and apps) will be applicable across international borders and will be evidenced-based, they will reduce the need for repetition in resource creation, reduce redundancy in resource creation (creating resources for topics that are not statistical issues), and therefore reduce resources (time, money and energy). With these future international resources, we will achieve stronger networking between countries and universities, reduce the need for independent work related to the duplication of resources, and the most important thing, provide attractive resources for this subject to make the knowledge sustainable and long-lasting for students.