REBUILDING
EDUCATION

Analysis

Anxiety is a major reason why students underperform in statistics courses. Studies consistently show that students with higher statistics anxiety receive lower grades on the statistics exam (Macher et al., 2012; Onwuegbuzie & Michael, 1995). Although, statistics anxiety is not only limited to the exam because statistics anxiety is “a performance characterized by extensive worry, intrusive thoughts, mental disorganization, tension, and physiological arousal when exposed to statistics content, problems, instructional situations, or  evaluative contexts” (Zeidner, 1991). One way that statistics anxiety hampers performance is by increasing procrastination, which means that students spend less time studying (Onwuegbuzie, 2004; Macher et al., 2013). These detrimental effects of statistics anxiety on performance are all the more troubling when considering the prevalence of statistics anxiety. In our survey, 64% of participants said they felt nervous when they found out that they would have to take statistics (Appendix ). Other studies find similarly high rates of statistics anxiety, ranging from 48% to 60%, especially among psychology students (Onwuegbuzie & Wilson, 2003; Ruggeri et al., 2008). In fact, the high prevalence of statistics anxiety among psychology students can partially be explained by the fact that students choose this subject because it seems to involve less math. For instance, Ruggeri and colleagues (2009) found that only 49% of psychology students knew that they would have to take statistics, which strongly suggests that psychology students underestimate the extent to which statistics plays a role in their field. Given the high prevalence of statistics anxiety among psychology students, it is clear that reducing statistics anxiety in this group is paramount.

At the VU, psychology students are exposed to statistics content in the second semester of their first year in the course Statistics 1. The course consists of lectures and workgroups. The primary goal of the workgroup is to help students with the final assignment by teaching them how to write a method and result section as well as how to use SPSS in order to perform various statistical operations (ex: t-test, correlation). Students are also given access to Grasple, previously called I Hate Statistics, an online learning platform that they can use in order to review core concepts and test their knowledge. In the last two years, various beneficial changes have been already been instituted that reduce statistics anxiety. One notable change is including Grasple, a revision tool that is extremely helpful according to students (Figure 1). Moreover, there is no denying that the current Statistics 1 course is effective. In our survey, we found that while most students (64%) felt anxious about taking statistics, after completing the course students reported that statistics is manageable (59%) and interesting (38.5%) (Appendix). Nonetheless, our group believes that more can be done in order to reduce statistics anxiety.

Figure 1: Students find Grasple (IHS) extremely useful

The current set-up of Statistics 1 has one major blindspot. Namely, statistics anxiety isn’t being measured at any point in the course, not even in the course evaluation. So, the scope of the problem is unknown given that it’s unclear how prevalent statistics anxiety is among VU psychology students. Yet, based on prior literature it’s safe to assume that statistics anxiety isn’t a negligible problem as 48% to 60% of students report high anxiety (Onwuegbuzie & Wilson, 2003; Ruggeri et al., 2008). To remedy this omission we propose incorporating an early identification screening that will help identify high anxiety students in need of extra support. Furthermore, while most students regard Grasple as being useful, we noticed that students are using the tool ineffectively. Specifically, students tend to use Grasple in the first week and the two weeks leading up to the exam. This trend is concerning because procrastination is major predictive factor for statistics anxiety (Macher et al., 2013). Consequently, there is need for a mechanism to incentivise students to work consistently through the material. Also, we believe that Grasple can be improved by switching from a simple list representation of core concepts to a mind-mind maps that provides the students with an overview. This might also boost students use of the tool. Lastly, the final assignment for the course alienates a majority of students who plan on pursuing a career in areas other than academic research.

The goal of our redesign is to lessen students statistics anxiety and we propose to accomplish this by addressing the limitations and omission of Statistics 1. Particularly, we support (1) developing an early screening procedure, (2) changing the layout of Grasple, (3) integrating a incentivization system for Grasple, and (4) giving students a choice for the final assignment.

Overview of Statistics 1 and our redesign:

Redesign

Early screening

The first element of our Redesign is a screening tool combined with a prevention mechanism for statistics anxiety. In order to reduce the negative effects of this anxiety on performance, we propose a screening method in the first 2 weeks of the course, combined with a low-cost intervention for at-risk students. In the second week of the course, students have already attended 2 to 4 lectures and had one workgroup. Although almost all students feel apprehensive prior to taking the course, we suspect that by week 2 a portion of students will be feeling more relaxed about it as they have gained some experience with the teachers and material. Thus, this is a good moment to identify those students who are still feeling anxious. Specifically, students will be given a survey through Canvas in early Week 2 of the period. This survey will be an adapted version of the Ellen Freedman Maths Anxiety Self-Test (example in Appendix). The questions on the survey will be adapted to pertain to statistics-relevant topics rather than mathematics in general. Students who score above 20 will be contacted via canvas with extra resources regarding their Statistics Anxiety. These will include: a message of support explaining that statistics Anxiety is common and unnecessary; instructions to use resources like Grasple as much as possible in order to make oneself comfortable around the material; and an invitation to sign up for 1 weekly extra workgroup with a tutor and peers in which students have the time to work on practice exercises at their own pace, and ask the tutor questions. Naturally, students who did not score high on the anxiety questionnaire but would also like to join the extra workgroup are welcome to do so- and are told to contact their tutor about this possibility. The advantage of using a questionnaire is that students can complete it in their own time, and in private, to avoid socially desirable responses. Furthermore, students who might be reluctant to ask for help (or perhaps do not even know that they need help) will fill in the questionnaire and be offered help, without having to solicit this.

In addition to this, workgroup tutors (workgroup teachers) will be instructed to spend 10 minutes in the first workgroup explaining to the students that it is very common to feel anxious about statistics, and that most students do. Furthermore, numbers should be shown indicated the proportion of students who have passed the class in the past. The intention of this is to put the students at ease and show them that much of their anxiety not only is common but is ungrounded.

GRASPLE

The second element of our redesign constitutes an electronic learning environment based on a network-structure that uses artificial intelligence to create a personal learning trajectory. The learning environment will be building on the software of Grasple, previously called I Hate Statistics. Currently, GRASPLE is built in a hierarchical structure, with students having to complete the material in a linear manner. In our redesign, the subject matter will be presented in a network, illustrated in figure 1. This network shows the connections between elements of the subject matter on an equal level, and the connections between elements that build up on each other. In order to mimic the 1-to-1 tutoring experience that is so pleasant to students, artificial intelligence, such as the software used by Knewton, will analyse the student’s performance. This software will use learning graphs to take over a tutor’s function of assessing where the basis lies of a student’s struggle with understanding the material. Based on this assessment, the software will customise the standard learning trajectory according to the student’s progress and difficulties. This learning trajectory will be based on the previously mentioned network. This aims to reduce students’ statistics anxiety through reducing the perceived size of the material. As the students will be able to recognise connections between material they have already learned and will learn, they will perceive the material less as an unconquerable obstacle, and more as the gradual build-up of connections it can be. A personal learning directory will also increase students’ sense of ownership over the material.

Figure 1: Network structure

A second objective of redesigning the makeup of Grapsle, is ensuring that students use it more consistently throughout the course. We have set up a collection of best practices. Firstly, providing weekly quizzes of multiple choice questions will help in fostering consistency. We advise these to be mandatory but not mandatory for those weekly timeframes, and we advise them to not count for the final grade. However, this will be for the course coordinator to decide. A second way in which consistency can be stimulated, is to provide a 0,5 point bonus at the end of the course for those students who have shown consistent work. This can be assessed through successful completion of the weekly quizzes. To prevent fraud, these quizzes will be based on a larger battery of multiple choice questions, with each student being presented a random selection of these questions. As a consequence, no test will be exactly alike. In order to challenge students to aim to truly understand the material on a deeper level, we recommend multiple choice questions that demand this level. Thus, the questions are not to consist of simple fact recital, but should require analysis and linking concepts. This online learning environment will not substitute face-to-face contact with teachers and peers. This will be taken care of during weekly working groups, where students will engage with the material in a small setting.

Final assignment

The third component of our redesign is altering the final assignment. Currently, students hand in the final assignment shortly after taking the exam. However, we think that students first need to pass the exam, and thereby demonstrate an understanding of statistics concepts, before proceeding to the final assignment. This proposition is based on students performance last year. After the exam in week 7, students have 3 weeks to write their final assignment with an additional option of receiving feedback. Students are given a choice between two assignments. On the one hand, working with SPSS and practising scientific writing, specifically how to write a methods and results section. On the other hand, choosing studies from an inventory of pre-selected studies and analyzing them in detail. All students learn how to use SPSS and the basics of scientific writing during the mandatory workgroups and by submitting a short mock paper in the first weeks of the course. Although, the main focus of the workgroups is reviewing material and real-world application of statistics. So, students who want to analyze an existing study in depth are at no disadvantage later on when it’s time to write their Bachelor thesis.

The goal of the alternative assignment is to foster students critical thinking in discerning which study is more credible and how to apply knowledge gleaned from studies. Before students can start thinking about the limitations and applications of a study, they first need to understand the study, which requires mastery of statistics. Students who choose to analyze an existing study will be given the following instructions:

1. Select 2 studies from the inventory of 6 studies about different treatments for depression.
2. Assignment components:

•  Summarize the research question and hypotheses posed by both studies
• Summarize information about the sample being studied (ex: number of participants, mean age)
• Describe the methods used in each study to test the hypothesis, be specific.
• Explain the results
  ○ Were the findings significant or non-significant?
  ○ What was the power, or effect size of the findings?
  ○ Do the studies have complementary or contradictory results?
• Identify two limitations for each study or questions that still need to be answered
• Which study is more reliable? Defend your position.
• Based on these studies what recommendations (if any) would you give a therapist treating patients with clinical depression?

Word limit: 1000 words

Justification

Early screening

The statistics anxiety screening element of our redesign can be justified through a neuroscientific perspective. Thorough examinations of maths anxiety and maths performance (arguably very comparable to statistics anxiety and performance) suggest a reciprocal relationship between the two. This means: bad maths performance (or, low maths ability) leads to increased maths anxiety; and high maths anxiety also leads to decreased maths performance. Maths anxiety impacts performance on a number of different levels: it causes maths avoidance, presumably due to the negative physiological symptoms of anxiety (sweating, increased heart rate, shortness of breath); it causes cognitive interference, a situation wherein the cognitive energy spent on the anxiety itself reduces the availability of resources necessary for concentration and working memory; and brain imaging data shows that anxiety interferes with performance in relation to brain regions associated with emotional regulation (Carey, Hill, Devine, & Szucs, 2016). The brain is a biological organ, and requires optimal conditions to learn. The increased levels of cortisol in the blood during anxiety prevent the brain from working at its optimal level. This pertains to many of the cognitive activities that are essential for learning, such as concentration, working memory, and cognitive engagement. Thus, the anxiety experienced by student around statistics is actively making their learning more difficult. In summary, we would argue that a statistics anxiety screening and intervention program is an essential element to a successful Statistics 1 course, as it helps students to engage more willingly with the material, and without being hindered by their own fear.

GRASPLE

Youssef El Bouhassani has developed the educational software mentioned in our redesign. We quote that he strongly believes this presentation of the material in a network instead of a linear hierarchy, as well as the creation of a personal learning trajectory helps students understand the material on a deeper level, create affiliation with the material as well as a sense of ownership, and helps increase motivation. Secondly, the stimulation of consistent work will combat procrastination. In previous implementations of Graspsle, it was seen that the software was used inconsistently throughout the course, showing a pattern of procrastination (figure 2). Procrastination has proven to be a causing factor of statistics anxiety (Dillon, 2014). Thus, it can be expected that stimulating consistent work will reduce students’ statistics anxiety. Thirdly, it has been proven that learning in a group with peers that differ in the level in which they master the material or intelligence is beneficial for each individual in that group. This is demonstrated by the big-fish-little-pond effect (Marsh, 2003). Also, it has shown that face-to-face contact to facilitate asking questions and explaining in real life is a highly effective way of learning. However, a reduction of contact-hours caused by the introduction of an online learning environment does not negatively impact adult students’ knowledge gain and satisfaction (Banks & Faul, 2007).

Figure 2:

Final assignment

Our idea of altering the final assignment is informed by two perspectives. In the article “Liberal Traditionalist” Peters outlines three necessary criteria that need to be met in order to consider teaching educating. They are: (1) transmission of worthwhile information, (2) knowing how the information being learned connects to other knowledge, and (3) the learner feels education is voluntary and not indoctrination. The final assignment now does not fulfil the first criteria because a majority of students do not feel like it’s intrinsically rewarding or relevant for their future. Similarly, the Vitruvian man shows that the current final assignment fits the extreme lower left quadrant that is characterized by rationality, science, and materialism (“Human Nature and Sustainability”). Yet, anything that is that far in the periphery becomes a caricature. We propose moving more towards the centre by taking into account that students have different interests and needs.

References

Anne C. Banks & Anna C. Faul (2007) Reduction of Face-to-Face Contact Hours in Foundation Research Courses: Impact on Students’ Knowledge Gained and Course Satisfaction, Social Work Education, 26:8, 780-793, DOI: 10.1080/02615470601140500

Carey, E., Hill, F., Devine, A., & Szucs, D. (2016). The chicken or the egg? The direction of the relationship between mathematics anxiety and mathematics performance. Frontiers in Psychology, 6(1987). doi:10.3389/fpsyg.2015.01987

H., P. K., & Dillon, D. B. (2014). Statistics Anxiety Update: Refining the Construct and Recommendations for a New Research Agenda. Perspectives on Psychological Science, 9(2), 196–208. https://doi.org/10.1177/1745691613518077

Macher, D., Paechter, M., Papousek, I., & Ruggeri, K. (2012). Statistics anxiety, trait anxiety, learning behavior, and academic performance. European journal of psychology of education, 27(4), 483-498.

Macher D., Paechter M., Papousek I., Ruggeri K., Freudenthaler H. H., Arendasy M. (2013). Statistics anxiety, state anxiety during an examination, and academic achievement. Br. J. Educ. Psychol. 83, 535–549. 10.1111/j.2044-8279.2012.0208 10.1111/j.2044-8279.2012.02081.x

Marsh, H. W., & Hau, K.-T. (2003). Big-Fish–Little-Pond effect on academic self-concept: A cross-cultural (26-country) test of the negative effects of academically selective schools. American Psychologist, 58(5), 364-376.
http://dx.doi.org/10.1037/0003-066X.58.5.364

Onwuegbuzie, A. J., & Seaman, M. A. (1995). The effect of time constraints and statistics test anxiety on test performance in a statistics course. The Journal of experimental education, 63(2), 115-124.

Onwuegbuzie, A. J. and Wilson, VA. 2003. Statistics anxiety: nature, etiology, antecedents, effects and treatments: a comprehensive review of the literature. Teaching in Higher Education, 8: 195–209.

Ruggeri, K., Díaz, C., Kelley, K., Papousek, I., Dempster, M., & Hanna, D. (2008). International Issues in Education. Psychology Teaching Review, 14(2), 65-74.

Zeidner, M. (1991). Statistics and mathematics anxiety in social science students: Some interesting parallels. British Journal of Educational Psychology, 61, 319–328.