Exploring Student Perceptions of the Use of Open Educational Resources to Reduce Statistics Anxiety

Article

Abstract

Numerous instructional strategies have been applied to minimize statistics anxiety. Instructors are likely to consider those strategies a burden and may hesitate to apply them in their courses if there is a lack of continuous support. Open educational resources (OERs) enabled by information and communication technology have the potential to resolve this concern owing to their cost-effectiveness and to the prolific collections available. OERs can be adopted through reuse, redistribution, revision, and remix. Although a few former studies proved that technology could effectively reduce statistics anxiety, fewer studies demonstrated the effective adoption of OERs through reuse, redistribution, revision, and remix when coping with statistics anxiety. The purpose of this study was threefold. First, from earlier studies, we identified instructional strategies used to reduce statistics anxiety. Second, according to those instructional strategies, we assisted instructors in selecting and customizing OERs through reuse, redistribution, revision, and remix and in applying them in introductory statistics/quantitative research methodology courses. Third, we investigated the students’ perceptions of the use of OERs to reduce statistics anxiety. The findings indicated that students had a positive reaction to the use of OERs to reduce statistics anxiety. Through this study, we can establish a rigorous approach to adopting and customizing OERs for various instructional needs in an interdisciplinary curriculum.

Keywords

Statistics anxiety Open educational resource Reuse Redistribution Revision Remix 

College students tend to view statistics as a least favorite subject (Hanna et al. 2008). Around 80% of the students in a statistics course are likely to experience various levels of statistics anxiety (Hanna et al. 2008) caused by the following six factors: (1) worth of statistics, (2) interpretation anxiety, (3) test and class anxiety, (4) computational self-concept, (5) fear of asking for help, and (6) fear of statistics teachers (Cruise et al. 1985). As indicated by former studies, multiple instructional strategies have been applied to reduce statistics anxiety. Some former studies applied statistics to real-world situations to improve the students’ opinions about the worth of statistics (Onwuegbuzie et al. 2010; Pan and Tang 2004). Others used journal writing (Dunn 2014; Dykeman 2011; Onwuegbuzie et al. 2010; Pan and Tang 2005) and excerpt activities (Rabin and Nutter-Upham 2010) to decrease interpretation anxiety. However, instructors may view these instructional strategies as a burden when applying them to actual courses if there is a lack of continuous support (Carolan and Guinn 2007; Miller 2010), which includes a supportive culture (e.g., open access to materials to avoid copyright issues) as well as constant availability of resources.

Open educational resources (OERs) are defined as free resources enabled by information and communication technologies for non-commercial purposes (UNESCO 2002). OERs embrace open access to materials and include a broad collection of materials, thus providing instructors with continuous support. Open access to materials involving open licensing associated with four permissions (i.e., reuse, redistribution, revision, and remix) allows users to freely share, exchange, and reproduce materials (Hilton et al. 2010; Wiley et al. 2014). A broad collection enables instructors to have alternative options in adopting OERs.

Although OERs enable instructors to flexibly customize these resourceful materials for different needs, few studies address how teachers adopt OERs through reuse, redistribution, revision, and remix to reduce statistics anxiety. Meanwhile, the students’ perceptions of the OERs used to reduce statistics anxiety are also unknown. The purpose of this study was threefold. First, from the former studies, we identified instructional strategies used to reduce statistics anxiety. Second, according to those instructional strategies, we assisted instructors in selecting and customizing OERs through reuse, redistribution, revision, and remix and in applying them to introductory statistics/quantitative research methodology courses. Third, we investigated the students’ perceptions of the use of OERs to reduce statistics anxiety. This current research presents a rigorous approach to adopting OERs in interdisciplinary courses and making statistics teaching and learning more affordable to both instructors and students.

Statistics Anxiety and Instructional Strategies

Statistics anxiety has been referred to as a certain level of anxious feelings that students experience as they learn statistics (Bell 2003; Onwuegbuzie 2000; Pan and Tang 2004, 2005). More than half of the students in a class may suffer various levels of statistics anxiety (Hanna et al. 2008). Such anxious feelings toward statistics learning can cause low motivation (Bell 2003; Onwuegbuzie 2000; Pan and Tang 2004, 2005) and lead to poor achievement (Macher et al. 2012). Six factors have been identified as the cause of statistics anxiety: (1) worth of statistics, (2) interpretation anxiety, (3) test and class anxiety, (4) computational self-concept, (5) fear of asking for help, and (6) fear of statistics teachers (Cruise et al. 1985).

Worth of Statistics

Statistics anxiety may happen when students consider statistics worthless. The students’ beliefs and views about statistics may determine the extent to which they are anxious about it and may determine their ways of learning (Murtonen et al. 2008). Students who consider research skills important for their future career tend to have a low level of statistics anxiety and use a deep learning approach to solve problems (Murtonen et al. 2008). In contrast, students who view statistics as irrelevant to their career development tend to have a high level of statistics anxiety (McGrath 2014; Murtonen et al. 2008) and procrastinate in taking statistics courses (Dunn 2014). In addition, students may feel studying statistics is useless due to its lack of connection with their daily life (Pan and Tang 2005) or profession (Lalayants 2012).

Instructional Strategies

Some instructional strategies are used to change the students’ perceptions of statistics, including making teaching methods application oriented (Pan and Tang 2004, 2005) and applying statistics to real-world situations (Onwuegbuzie et al. 2010; Pan and Tang 2004) or the students’ professions (Lalayants 2012). Application-oriented teaching methods include applying statistics to research projects and reinforcing concepts through homework (Pan and Tang 2004, 2005). Through a journal excerpt activity which asks students to briefly summarize methodology sections in empirical studies, students can effectively build the connection between statistical concepts and their real-world applications (Rabin and Nutter-Upham 2010). Relating course work and examples to the students’ fields of study (Lalayants 2012), personal lives (Lesser and Reyes 2015), or professions can also improve their negative perception of statistics (Lalayants 2012). Students may consider statistics interesting if it connects with their personal lives through songs or jokes (Lesser and Reyes 2015).

Interpretation Anxiety

Statistics anxiety may occur if students have difficulty in interpreting quantitative-based findings in scholarly reports or papers (Vigil-Colet et al. 2008). Onwuegbuzie (2000) indicated that interpretation anxiety was one of the two most prevalent factors causing statistics anxiety across multiple age groups of students (Bui and Alfaro 2011). Interpretation anxiety mainly affects the students’ abilities to fully understand research articles as well as to analyze and interpret statistical data (Onwuegbuzie 2000). In addition to feeling intimidated when dealing with formulas and statistical calculations (Lalayants 2012), students are also challenged by how to write a conclusion to interpret their findings (McGrath 2014).

Instructional Strategies

Some instructional strategies are used to reduce interpretation anxiety, including journal writing (Dunn 2014; Dykeman 2011; Onwuegbuzie et al. 2010; Pan and Tang 2005), journal excerpt exercises (Rabin and Nutter-Upham 2010), and problem creation activities (Kolar and McBride 2003). The activities of journal writing and excerpt exercises are used to foster basic skills in analyzing figures and tables and to enhance overall comprehension of difficult statistical concepts (Rabin and Nutter-Upham 2010). Through a problem creation activity, students can learn the entire process of conducting a research study, including selecting appropriate tests to answer research questions, interpreting test results, and writing suitable summaries of results and conclusions (Kolar and McBride 2003).

Test and Class Anxiety

When students walk into a room to take a statistics exam, their statistics anxiety likely occurs before and during tests (Macher et al. 2013; Vigil-Colet et al. 2008). Test and class anxiety serves as the primary source of statistics anxiety (Onwuegbuzie 2000) and likely happens across age groups (Bui and Alfaro 2011). Owing to their unfamiliarity with course contents as well as to the difficulty of linking theory with practice, more than 75% of the students feel uncomfortable when dealing with statistics formulas and calculations (Lalayants 2012; Murtonen et al. 2008). Statistics anxiety caused by test and class anxiety has a negative relationship to the students’ perceived value of statistics and to their prior math experiences (Cybinski and Selvanathan 2005).

Instructional Strategies

The instructional strategies for dealing with test and class anxiety fall into three categories: (1) assessment design, (2) curriculum and course design, and (3) instructional methods. Relevant instructional strategies are displayed in Table 1. Assessment design involves formative and summative assessments. Formative assessments help students understand computational concepts and serve as a good indicator to check the students’ mastery of materials. Summative assessments can increase the students’ familiarity with the test format and response options (Pan and Tang 2005) and assist instructors in using different methods to evaluate student performance (Onwuegbuzie et al. 2010). Curriculum and course design can bridge the gap between theory and application (Neumann et al. 2011), enhance student learning for higher order conceptual knowledge (Lloyd and Robertson 2012), and improve problem solving skills (Lloyd and Robertson 2012). Instructional methods primarily include the pedagogical approaches that instructors can utilize in class.
Table 1

List of instructional strategies to cope with test and class anxiety

Categories

Strategy

Assessment design

Formative assessment

- Mini assignments (Lalayants 2012)

- Pre-lecture quizzes (Brown and Tallon 2015)

- Practice problems (Kolar and McBride 2003)

- Portfolio (Sciutto 2002)

Summative assessment

- Non-graded tests (Dykeman 2011)

- Open-book/note exams (Onwuegbuzie et al. 2010)

- Performance assessments (Onwuegbuzie et al. 2010)

Curriculum and course design

- Break down concepts into lay terms (Lalayants 2012)

- Provide clear definition of formulas (Lalayants 2012)

- Tailor the course contents to match student backgrounds and needs (Lalayants 2012)

- Share lecture notes and other materials with students on the learning management system (Lalayants 2012)

- Apply essay writing and journal article critiquing activities into courses (Pan and Tang 2004)

- use current news to explain basic statistical concepts and methodological issues in research (Onwuegbuzie et al. 2010)

- Use pass/fail basis as an alternative option to evaluate student performance (Pan and Tang 2005)

- Use screencast tutorials and video tutorials (Lloyd and Robertson 2012) or games (Boyle et al. 2014)

- Focus on practical applications and real-world examples (Pan and Tang 2005)

Instructional methods

- Repeatedly explain details of course (Lalayants 2012)

- Provide prompt feedback (Bell 2003)

- Use humor during courses (Lalayants 2012)

Computational Self-Concept

Computational self-concept relates to individual ability in math. Statistics anxiety may occur when students are not confident in their ability to solve mathematical problems in a statistics class (Onwuegbuzie 2000; Pan and Tang 2004, 2005). Some students may feel anxious about statistics due to a lack of knowledge in mathematics, low prior achievement, bad experiences in previous mathematics courses, and a fear of mathematics (Lalayants 2012; McGrath 2014; Murtonen et al. 2008; Onwuegbuzie 2000; Pan and Tang 2004). In former studies, good performance in mathematics was shown to lead to positive statistics learning experiences and attitudes (Mills 2004). Therefore, students with a higher level of mathematical self-concept indicated a lower level of statistics anxiety (Macher et al. 2013). In addition, prior experience in mathematics may determine the students’ ways of learning in a quantitative research methodology course (Murtonen et al. 2008). For example, students are likely to do as little as they can in quantitative research methodology courses involving numbers or mathematical calculations if they have had some negative experiences in prior mathematics courses.

Instructional Strategies

The use of basic steps is suggested to reduce student anxiety caused by computational self-concept (Lalayants 2012). In particular, instructors can pace the course more slowly when teaching the math components. For example, instructors can break down concepts into lay terms, provide clear definitions of formulas, and go over math contents slowly.

Fear of Asking for Help

Statistics anxiety may occur when someone fears to look for help in learning statistics (Onwuegbuzie 2000). If students are reluctant to ask for assistance, they tend to have moderately high statistics anxiety. Additionally, the fear of asking for help has become one of the primary challenges that instructors face when teaching quantitative research methodology courses (McGrath 2014).

Instructional Strategies

Several instructional strategies are used to reduce the fear of asking for help and are displayed in Table 2. Peer assistance is primarily used to provide adequate support (Lalayants 2012). A supportive learning environment provides students with rich resources (e.g., sample questions) and allows them to freely ask questions or express their ideas (Groeneboom et al. 1996). Instructors can apply flexible instructional strategies such as one-on-one discussion inside and out of the classroom to provide extensive feedback (Onwuegbuzie et al. 2010). Students can receive school-wide support from special programs such as a statistics tutor program (McGrath 2014) and check-in program (Manalo and Leader 2007; McGrath 2014).
Table 2

List of instructional strategies to cope with the fear of asking for help

Strategy

Example

Providing adequate support (Lalayants 2012)

- Peer assistance

 ○ Study group (Lalayants 2012)

 ○ Conversation partners (Bell 2003)

 ○ Out-of-classroom or one-to-one tutoring sessions (Lalayants 2012)

 ○ Computer lab sessions (Lalayants 2012)

Creating a supportive learning environment (Groeneboom et al. 1996)

- Include rich resources (e.g., sufficient examples and sample problems) (Groeneboom et al. 1996)

- Allow students to freely express ideas (Groeneboom et al. 1996) and ask questions (Bell 2003)

Applying flexible instructional strategies (Pan and Tang 2005)

- Provide availability of assistance (Pan and Tang 2005)

- Offer extensive feedback either in a one-on-one or face-to-face discussion (Onwuegbuzie et al. 2010)

- Share solved copies of the examination (Bell 2003)

Offering special program support (Manalo and Leader 2007; McGrath 2014)

- Check-in program (Manalo and Leader 2007; McGrath 2014)

- Statistics tutor program (McGrath 2014)

Fear of Statistics Teachers

Statistics anxiety can be caused by the students’ fear of their statistics teachers (Onwuegbuzie 2000). Instructor attitude (Pan and Tang 2005) and instructor immediacy (McGrath 2014) toward students can directly relate to this fear. If instructors are sensitive to the students’ concerns and aware of their worries, statistics anxiety can be reduced to help students learn statistics more effectively (Pan and Tang 2005). Instructor immediacy, including smiling, making eye contact, and speaking at a close range, can also make students feel less anxious about statistics instructors (McGrath 2014).

Instructional Strategies

Five instructional strategies are used to reduce the students’ fears of their statistics teachers and are displayed in Table 3. These strategies can help instructors create a positive atmosphere for the entire course and likely change the students’ negative perceptions of statistics teachers.
Table 3

List of instructional strategies to cope with the fear of statistics teachers

Strategy

Example

Being attentive to the students’ statistics anxiety (Lalayants 2012)

- Acknowledge student fear and the intimidating nature of instructor (Lalayants 2012; Onwuegbuzie et al. 2010; Pan and Tang 2004, 2005)

- Explore the students’ negative feelings and perceptions of statistics courses (Lalayants 2012)

Being flexible with students (Onwuegbuzie et al. 2010)

- Offer extra tutoring (Lalayants 2012)

- Provide flexible office hours (Pan and Tang 2004, 2005)

Providing encouragement to students (Pan and Tang 2005)

- Give continuous encouragement (Lalayants 2012; Onwuegbuzie et al. 2010; Pan and Tang 2004, 2005)

- Show empathy (Onwuegbuzie et al. 2010)

- Give personal recognition to students (McGrath 2014)

Displaying a positive attitude (Onwuegbuzie et al. 2010)

- Cultivate an approachable teaching presence (Pan and Tang 2005) to convey immediacy such as smiling, making eye contact, and speaking at a close distance

- Use humor (Lalayants 2012; Pan and Tang 2004, 2005)

Implementing a humorous teaching style (Pan and Tang 2005)

- Adopt humorous cartoon examples into courses (Onwuegbuzie et al. 2010; Pan and Tang 2005)

Use of Technology to Reduce Statistics Anxiety

Some former studies (Boyle et al. 2014; Chance et al. 2007; Lloyd and Robertson 2012; Neumann et al. 2012) reported that the use of technology could effectively reduce statistics anxiety caused by the six factors mentioned previously. For example, interactive computer-based simulations which utilized visualization (Boyle et al. 2014) improved the students’ comprehension of abstract concepts and made a first-year statistics course more practical to them (Neumann et al. 2012). Graphing calculators assisted students in concentrating on result interpretation, rather than formula computation (Chance et al. 2007). Video tutorials were used to help students improve conceptual understanding and develop effective problem-solving skills (Lloyd and Robertson 2012). Students could explore and solve real-world problems through the use of technology (Groeneboom et al. 1996).

Effective Use of Open Educational Resources

The Internet offers easy access to open resources such as Wikipedia, YouTube, and Flickr, which are mostly free. The educational use of open resources, defined as open educational resources, is enabled by information and communication technologies and adapted for non-commercial purposes (UNESCO 2002). OERs have been applied in a variety of contexts (Clements and Pawlowski 2012; Hockings et al. 2012; Lane and McAndrew 2010; Olcott 2012) and have broadly covered a variety of materials (Clements and Pawlowski 2012; D’Antoni 2009; Lane and McAndrew 2010). OERs include open access to materials and rich resources. These two features serve as the salient advantages that OERs bring to education, and they provide continuous support to instructors. Open access to materials allows users to freely share, exchange, and reproduce open educational resources without violating copyright laws (Caswell et al. 2008; Clements and Pawlowski 2012; Olcott 2012; Rogerson-Revell 2007; Wiley and Gurrell 2009). In addition to the cost-saving features, OERs can be flexibly customized to meet instructional needs. In comparison with copyright-restricted books, for example, open textbooks have proven to be more likely to meet the K-12 teachers’ needs in term of cost, quality, and flexibility in customization (Kimmons 2015). The rich resources that OERs provide give a wealth of alternative options to users (D’Antoni 2009; Olcott 2012).

Reuse, Revision, Redistribution, and Remix

Depending on their license, OERs enable users to adopt them in any of the following four ways: (1) reuse, (2) redistribution, (3) revision, and (4) remix. Reuse indicates that users are allowed to reuse entire or partial open educational resources. Redistribution allows users to legally share these resources with other users. In addition, users may give permission to others to revise or modify OERs. Remix allows users to combine OERs with various existing resources for new purposes. Remix has made OERs meaningful in some specific situations (Amiel 2013). For example, the remix of two open textbooks was used to improve the students’ perceptions of the quality and usefulness of OERs (Hilton et al. 2013). These four approaches make OERs more adaptable to some specific situations such as decreased costs or a need for improvements in the students’ learning (Hilton et al. 2010).

Research Questions

The use of technology such as interactive computer-based simulations (Boyle et al. 2014) and video tutorials (Lloyd and Robertson 2012) can effectively reduce statistics anxiety. However, only a few empirical studies have presented the adoption of OERs and shown their effects in coping with statistics anxiety (Bowen et al. 2014; Lovett et al. 2008). There is a need for better understanding of how instructional strategies in adopting OERs through reuse, redistribution, revision, and remix can effectively reduce statistics anxiety. Additionally, we also need to know how students perceive the use of OERs to reduce statistics anxiety. Particularly, to date, most OERs are simply adopted through reuse. Revision and remix of OERs have a relatively low adoption rate (Duncan 2009; Hilton et al. 2012; Petrides et al. 2008). This has become one unresolved issue for OER adoption in multiple domains (Fischer et al. 2015). Therefore, the following questions were used to guide this research study:
  1. 1.

    What are the instructional strategies used in adopting OERs through reuse, redistribution, revision, and remix to reduce statistics anxiety?

     
  2. 2.

    How do students perceive the use of OERs to reduce statistics anxiety?

     

Methods

We used a mixed method to evaluate the students’ perceptions of the use of OERs to reduce statistics anxiety. A mixed method was chosen, because it took advantage of both quantitative (large sample size, trends, generalization) and qualitative (details, in depth) methods to deal with the weaknesses in each single method (Creswell and Clark 2007). After OERs were used in the lecture and lab sessions, we collected quantitative and qualitative data concurrently from (1) the student survey about using OERs to reduce statistics anxiety and (2) the students’ responses to open-ended questions.

Participants

The participants in this study were students who were enrolled in five introductory statistics/quantitative research methodology courses at a university located in the southeastern United States. These participants came from different programs and departments. There were 113 participants in total. Eighty percent of the participants were between 18 and 30 years old. Fourteen percent of the participants were between 31 and 40. The remaining 6% were over 40. All participants in this study were pursuing an undergraduate or graduate degree at the time this study was being conducted. Before the intervention, we adopted the Statistics Anxiety Rating Scale (Cruise et al. 1985) to measure the participants’ statistics anxiety. A total of 22 items on a five-point Likert scale ranging from (1) no anxiety to (5) very much anxiety were used. Test and class anxiety (M = 3.22, SD = 1.14) was the primary source of statistics anxiety compared with others, namely, interpretation anxiety (M = 2.77, SD = .85), fear of statistics teacher (M = 2.59, SD = 1.09), fear of asking for help (M = 2.38, SD = 1.19), computational self-concept (M = 2.24, SD = 0.89), and worth of statistics (M = 1.93, SD = 0.64).

Open Educational Resources

All the OERs we adopted in this study were collected from 12 OER repository websites (see Table 4). Ten out of 12 websites had an interdisciplinary collection. Only two websites, Statistics Online Computational Resource (SOCR) and Online Statistics Education: An Interactive Multimedia Course of Study, were primarily used for statistics teaching and learning. The OERs used in this study included scholarly articles, datasets, simulations, video/screencast tutorials, comics or cartoons, and lesson plans.
Table 4

List of OER repositories

OER repository

Collection

Connexions (http://cnx.org/)

More than 17,000 learning objects or modules and over 1000 collections (textbooks, journal articles, etc.) are used.

CAUSEweb.org (https://www.causeweb.org/resources/)

This site mainly provides support in four areas: resources, professional development, outreach, and research.

Multimedia Educational Resource for Learning and Online Teaching (MERLOT) (http://www.merlot.org/merlot/index.htm)

A free and open peer-review process is used to evaluate the quality of OERs.

Khan Academy (https://www.khanacademy.org/)

Khan Academy provides a collection of videos which can be freely used by students and teachers.

The Sofa Open Content Initiative (http://sofia.fhda.edu/gallery/statistics/index.html)

The resources are created by a Learning Technology & Innovations program at a community college in California.

Saylor.org (http://www.saylor.org/)

More than 300 courses are free and available.

Statistics Online Computational Resource (SOCR) (http://www.socr.ucla.edu/)

All resources are primarily used for statistics learning and teaching. The collection broadly covers games, experiments, wikis, hands-on activities, etc.

Open Learn (http://www.open.edu/openlearn/)

More than 650 courses are free and available.

Online Statistics Education: An Interactive Multimedia Course of Study (http://www.oercommons.org/courses/online-statistics-an-interactive-multimedia-course-of-study/view)

This website has a collection for users to learn and teach introductory statistics and includes materials such as textbooks and video presentations.

Curriki (http://www.curriki.org/welcome/)

More than 50,000 resources are free and available.

HippoCampus.org (http://www.hippocampus.org/HippoCampus/)

There is a variety of multimedia content (e.g., videos, animations, and simulations). These resources are available for users in K-12, postsecondary, and higher education.

OpenCourseWare Consortium (http://www.ocwconsortium.org/about-ocw/)

A variety of resources such as syllabi and textbooks are available at this website. Also, the website has coordinated with other universities to develop and publish these resources.

Scholarly articles adopted in this study were mainly published papers with open access. An Experiential Approach to Integrating ANOVA Concepts is one published article shared in MERLOT (see Fig. 1). Instructors can directly reuse this article in their courses and share it with students.
Fig. 1

Scholarly article

Some open datasets are made public and shared in the OER repositories. Using Cigarette Data for An Introduction to Multiple Regression is an open dataset shared in MERLOT (see Figs. 2 and 3). Users can reuse a partial or an entire dataset for further data analysis.
Fig. 2

Open dataset

Fig. 3

Open dataset instruction

Simulations used in this study served as a teaching aid to help students understand some abstract concepts. Linear Regression is one simulation shared in CAUSEweb.org (see Fig. 4). In Fig. 4, users randomly add black dots into the left pane. Then, a red regression line and green formula are displayed in the left pane. In the right pane, the residual variation indicates how close each added dot is to the regression line.
Fig. 4

Regression simulation

Video and screencast tutorials primarily provide step-by-step demonstrations. Videos such as Regression collected from Khan Academy allow students to flexibly review some important steps when analyzing data (see Fig. 5).
Fig. 5

Video in Khan Academy

Free comics (e.g., Data Miners) or cartoons (e.g., Divorce Lawyer) collected from CAUSEweb.org can bring humor to classes and enable students to feel less anxious about statistics courses (see Figs. 6 and 7).
Fig. 6

Data miner comic

Fig. 7

Divorce cartoon

Some lesson plans are available in the OER repositories. Rather than directly adopting the entire lesson into courses, instructors can redesign partial activities and assessments for other purposes (e.g., in-class activities, assignments, quizzes). CSR Lesson Plan—Applications of Linear Regression is a lesson plan collected from Curriki. Instructors can download the entire Microsoft-Word lesson plan and revise it to meet instructional needs (see Figs. 8 and 9).
Fig. 8

CSR lesson planapplications of linear regression

Fig. 9

CSR lesson plan (Microsoft-Word document)

Intervention

In this study, we shared the instructional strategies with three instructors who taught these five introductory statistics/quantitative methodology courses in the spring of 2014. These instructional strategies were applied in both lecture and lab sessions (see Table 5). There was a total of 16 to18 weeks in each course. Students in each course were required to take both a 3-hour lecture and a 1-hour lab session every week. We worked closely with each instructor to select OERs from the 12 OER repositories. We also assisted instructors in customizing these OERs for different instructional purposes (e.g., in-class or lab activities, quizzes, or assignments) in order to precisely align with instructional strategies to reduce statistics anxiety.
Table 5

List of instructional strategies adopting open educational resources

Factors causing statistics anxiety

Instructional strategies from literature

Instructional strategies adopting OERs

Worth of statistics

Application of statistics to real-world situations (Onwuegbuzie et al. 2010; Pan and Tang 2004)

Some figures and tables in scholarly articles were reused as examples in the lecture sessions to effectively connect course work with relevant professions.

Statistics connection with the students’ professions (Lalayants 2012)

Interpretation anxiety

Problem creation activity (Kolar and McBride 2003)

Instructors reused partial open datasets in lab sessions to demonstrate how to run data analysis and interpret results. Additionally, instructors revised partial datasets for other purposes (e.g., exams and homework).

Test and class anxiety

Curriculum and course design (Boyle et al. 2014; Lalayants 2012; Onwuegbuzie et al. 2010; Pan and Tang 2004, 2005)

Some simulations were reused in lecture sessions. Simulation hyperlinks were also shared with students on the learning management system.

Video tutorials were reused in lab sessions and freely shared with students. When students did their assignments, they also watched these video tutorials to check if there were any missing steps in data analysis.

Instructional methods (Bell 2003; Lalayants 2012)

Free statistics comics or cartoons including fun and humor were reused during lecture sessions.

Computational self-concept

The use of basic steps (Lalayants 2012)

Screencast tutorials including step-by-step demonstrations were reused and shared with students as they worked on assignments.

Fear of asking for help

Providing adequate support (Lalayants 2012)

Videos or screencast tutorials supporting students in and outside of the classroom were reused or shared with students.

Creating a supportive learning environment (Groeneboom et al. 1996)

Instructors looked for some examples and sample questions from lesson plans and redesigned them for in-class activities or quizzes.

Fear of statistics teachers

Implementing a humorous teaching style (Pan and Tang 2005) to display a positive attitude (Onwuegbuzie et al. 2010)

Statistics comics including fun and humor were reused in lecture sessions.

Data Collection Procedure

The data were collected during a 16-to-18-week period. OERs were primarily used in course lectures and lab sessions from the first week of class. In the 15th week, a satisfaction survey was used to measure the students’ perceptions of OERs to reduce statistics anxiety.

Measures

We included qualitative and quantitative data to measure the students’ perceptions of OERs to reduce statistics anxiety. First of all, three open-ended questions asked students about their use of OERs during course lectures and lab sessions. For example, students were asked to complete the sentence “I think that using open educational resources in this course is useful for___.” In addition, a satisfaction survey including Likert scale items was used to measure the students’ perceptions of OERs to reduce statistics anxiety caused by the six factors (i.e., worth of statistics, interpretation anxiety, test and class anxiety, computational self-concept, fear of asking for help, and fear of statistics teachers). Students were asked, for example, to rate their agreement with the statement “I think using open educational resource in this course can help decrease my anxiety in interpreting the quantitative data.” A total of six items on a five-point Likert scale ranging from (1) strongly disagree to (5) strongly agree were used. The reliability in this study was 0.81 of Cronbach’s alpha value.

Data Analysis

The qualitative data were analyzed through content analysis (Creswell and Clark 2007). In particular, we classified the students’ responses into six categories matching the six factors causing statistics anxiety. In each category, we counted the number of students who had a positive perception of the use of OERs to reduce statistics anxiety and summarized these responses. Additionally, we used descriptive statistics to examine the mean, standard deviation, and correlation of all measured variables in the quantitative data. In order to triangulate our findings, we identified which pairs of measured variables were highly correlated in the quantitative data. Then, we used qualitative data to calculate the number of students who commented positively on the use of OERs to reduce statistics anxiety caused by this pair of variables. In this way, we could triangulate both quantitative and qualitative results to precisely evaluate the students’ perceptions of the use of OERs to reduce statistics anxiety.

Results

From the literature, we identified multiple instructional strategies used to reduce statistics anxiety caused by the six factors. In consideration of the six types of OERs utilized in the study, we decided on the following seven instructional strategies adopting OERs through reuse, redistribution, revision, and remix: (1) reuse figures and tables from scholarly articles, (2) reuse and revise an open dataset, (3) reuse simulations and redistribute their hyperlinks, (4) reuse and redistribute video tutorials, (5) reuse free statistics comics or cartoons, (6) reuse and redistribute screencast tutorials, and (7) remix examples and sample questions from lesson plans into regular assessments.

From the three open-ended questions, the students’ responses regarding the use of OERs are summarized in Table 6. These responses are classified into six categories relevant to the six factors causing statistics anxiety. In addition, descriptive statistics including mean, standard deviation, and correlation for measured variables used in this study are presented in Table 7.
Table 6

Summary of students’ responses toward the use of OERs

Factors causing statistics anxiety

Number of students (N)

(percentage)

Summary of the students’ responses

Worth of statistics

N = 25

(22.94%)

- Provide a practical approach for people who don’t regularly or frequently work with statistics to learn this subject

- Help students understand the ways to apply this research methodology in the real-world situations

- Help people find jobs requiring statistics skills

Interpretation anxiety

N = 18

(16.51%)

- Complement individual analysis skills

- Enhance problem-solving skills

Test and class anxiety

N = 96

(88.07%)

- Improve comprehension

- Fill the gap between what is known and what should be learned

- Supplement statistics learning

- Promote gains in knowledge and information

- Substitute outdated materials (e.g., books)

- Provide access to resources anytime and anywhere

Computational self-concept

N = 37

(33.94%)

- Help students visualize concepts

- Depict complicated concepts

fear of asking for help

N = 24

(22.02%)

- Help students find answers before asking instructors questions

- Provide additional support

- Provide study aids for people having difficulty in statistics learning

- Provide assistance in individual and dissertation studies

Fear of statistics teachers

N = 12

(11.01%)

- Provide humor with students

- Attract student attention

- Make students feel comfortable learning statistics

- Keep statistics courses interesting and engaging

- Improve the learners’ motivation

- Make students less stressed about statistics learning

Table 7

Mean, standard deviation, and correlation for measured variables

Variables (the students’ perceptions of OERs used to reduce statistics anxiety caused by six factors)

M (SD)

1

2

3

4

5

6

 1. Worth of statistics

4.05 (0.79)

     

 2. Interpretation anxiety

3.79 (0.92)

0.36**

    

 3. Test and class anxiety

3.93 (0.85)

0.29**

0.75**

   

 4. Computational self-concept

4.07 (0.73)

0.51**

0.68**

0.68**

  

 5. Fear of asking for help

3.69 (0.96)

0.32**

0.32**

0.28**

0.38**

 

 6. Fear of statistics teachers

3.42 (0.98)

0.21*

0.37**

0.38**

0.30**

0.48**

*p < .05; **p < .01

The Students’ Responses Toward the Use of OERs to Reduce Statistics Anxiety

Twenty-three percent of the students (N = 25) expressed that the use of OERs was helpful for their future career or profession, stating, for example, “I may get hired or have a better career in the field because OERs used in the courses help me learn statistics in a practical way.” A few students mentioned that the use of OERs in courses could be useful in their professions, making statements such as, “the use of OERs in the courses can help us understand statistics better and incorporate it into the business world.”

Seventeen percent of the students (N = 18) believed that OERs used in the courses could prepare them with better research or problem-solving skills as they conducted individual research or dissertation studies. One student said, “I think using open educational resources in this course can help me to practice concepts pertaining to ANCOVA outside of the classroom so that I can have a better grasp of how to perform certain skills when I begin to do my own research and statistical analyses.”

Eighty-eight percent of the students (N = 96) expressed that OERs were primarily perceived as an aid to reinforce knowledge and improve comprehension. One student specifically stated, “I think using open educational resources in this course can help me become more confident in my ability to master course contents and skills and to complete assignments with greater accuracy.” 35.7% (N = 39) of students mentioned that OERs could improve or enhance individual statistics learning, and one commented, “Using open educational resources in this course could help me better learn the materials since I never had to try to cram them all into my brain before a test.”

Thirty-four percent of the students (N = 37) indicated that the use of OERs could help them better understand the hard concepts. For example, one person said, “I think using open educational resources in this course could help me stay engaged and remember concepts.”

Twenty-two percent of the students (N = 24) perceived OERs as a study aid or as an option for them to look for support. Four out of 24 students expressed that the use of OERs could assist them in looking for answers before they directly asked an instructor for help. Moreover, two students mentioned that they might become more independent learners due to the use of OERs. One student indicated that “the continual use of OERs can support individual statistics learning, even after the class is done.”

Eleven percent of the students (N = 12) expressed that the use of OERs has attracted their attention and brought fun, enjoyment, and humor to the courses. One student commented, “I think that using OERs in the course helps me study with a lot of fun and makes the course enjoyable.”

Descriptive Statistics for All Measured Variables

The mean in all measured variables points out the students’ positive perceptions of the use of OERs to reduce statistics anxiety. The Pearson correlation indicates that the students’ perceptions of OERs used to reduce statistics anxiety caused by the six factors can positively and significantly correlate with each other (see Table 7). That is, when students positively perceive the use of OERs to reduce statistics anxiety caused by the worth of statistics, they tend to have a similar perception about the use of OERs to decrease their statistics anxiety caused by computational self-concept. The students’ perceptions of OERs used to reduce statistics anxiety caused by the following four pairs of variables had correlation values larger than 0.5: (1) interpretation anxiety and test and class anxiety (r = .75), (2) worth of statistics and computational self-concept (r = .51), (3) interpretation anxiety and computational self-concept (r = .68), and (4) test and class anxiety and computational self-concept (r = .68).

Triangulation of Qualitative and Quantitative Data

The correlation value for the students’ perceptions of OERs used to reduce statistics anxiety caused by interpretation anxiety and test and class anxiety was 0.75. From the qualitative data, 14.6% (N = 16) of the students positively viewed the use of OERs to reduce statistics anxiety caused by these two factors. The correlation value for the students’ perceptions of OERs used to reduce statistics anxiety caused by worth of statistics and computational self-concept was 0.51. Our findings from the open-ended questions indicated that 9% (N = 10) of the students positively commented on the use of OERs to reduce statistics anxiety caused by this pair of factors. The students’ perceptions of OERs used to reduce statistics anxiety caused by interpretation anxiety and computational self-concept had a correlation of 0.68. According to the students’ responses in open-ended questions, 5.5% (N = 6) of the students indicated that OERs used in courses could be helpful to reduce statistics anxiety caused by this pair of factors. The correlation value for the students’ perceptions of OERs used to reduce statistics anxiety caused by test and class anxiety and computational self-concept was 0.68. 27.5% (N = 30) of students positively commented on the use of OERs to reduce statistics anxiety caused by this pair of factors.

Discussion

In this study, we identified the instructional strategies for adopting OERs through reuse, redistribution, revision, and remix to reduce statistics anxiety and implemented them into five introductory statistics/quantitative research methodology courses. We also investigated the students’ perceptions of OERs used to reduce statistics anxiety caused by the six factors. Our findings point out the positive outcomes of the use of OERs to reduce statistics anxiety. Details of study findings, implications, and limitations are discussed in the following sections.

Our qualitative findings indicated that more than 30% of the students viewed the use of OERs as helpful to reduce statistics anxiety caused by test and class anxiety and computational self-concept. From the quantitative findings, the students’ perceptions of OERs used to reduce statistics anxiety caused by the following four factors had a high correlational relationship: (1) worth of statistics, (2) interpretation anxiety, (3) test and class anxiety, and (4) computational self-concept. From the data triangulation, 27% (N = 30) of the students considered the use of OERs effective to reduce statistics anxiety caused by test and class anxiety and computational self-concept. 14.6% (N = 16) of the students positively commented on the use of OERs to deal with statistics anxiety caused by test and class anxiety and interpretation anxiety.

Onwuegbuzie and Wilson (2003) discovered that test and class anxiety tended to be the primary source of statistics anxiety compared with others, namely, worth of statistics, interpretation anxiety, computational self-concept, fear of asking for help, and fear of statistics teacher. Therefore, the OER adoption through reuse, redistribution, revision, and remix in this study, to some extent, was likely to have some significant effects on the test and class anxiety, interpretation anxiety, and computational self-concept. Particularly, the OERs used to reduce statistics anxiety caused by these three factors included open datasets, simulations, video/screencast tutorials, and comics or cartoons. Although open datasets were revised for other purposes such as exams or assignments, instructors primarily reused and shared OERs with students to cope with statistics anxiety caused by these three factors.

Implications

In this study, we aimed to effectively apply instructional strategies adopting OERs through reuse, redistribution, revision, and remix to reduce statistics anxiety. The open access to materials and rich collection that OERs bring to education not only provide instructors with continuous support but also make statistics learning more affordable for students. This study can serve as an example for instructors and instructional designers to effectively select and customize OERs for various instructional needs. Through this study, we can demonstrate a rigorous approach to adopting OERs in interdisciplinary courses.

Limitations

The main limitation in this study is a lack of multiple data collection methods. All the data were self-reported by students who voluntarily participated in this study. There were no actual observations and interviews in this study. The lack of multi-method data collection may cause limitations in interpreting findings for some specific issues in the study. For example, the three measured variables about the students’ perceptions of OERs used to reduce statistics anxiety caused by interpretation anxiety, test and class anxiety, and computational self-concept had a high correlational relationship. However, correlation does not imply causation. We are not able to identify the casual relationship among these three measured variables and to interpret this finding with sufficient information.

Future Study

In this study, we investigated the students’ perceptions of the use of OERs which were reused, redistributed, revised, and remixed to reduce statistics anxiety. These four approaches make OERs more adaptable to some specific needs and thus lead to three different levels of openness (Hilton et al. 2010). The first level of openness which represents the most fundamental level is the reuse of OERs. Reuse and redistribution of OERs represent the second level. A mix of reuse, revision, remix, and redistribution can represent the highest level of openness. As indicated by Amiel (2013), remixing increases the usefulness of resources in some specific situations. In a future study, we will measure the effectiveness of adopting OERs at each level of openness. We will also identify if there is any difference in the students’ learning outcomes when OERs are adopted at the three different levels of openness. In addition, we will investigate the barriers instructors face when adopting OERs at each level of openness.

Conclusion

This study reflects a formative design process within an iterative cycle (i.e., design, development, implementation, and evaluation) when adopting OERs and applying them in the instructional strategies to reduce statistics anxiety. Due to a lack of continuous support, instructors may hesitate to apply instructional strategies in actual courses to reduce statistics anxiety. OERs, which provide open access to materials, a high flexibility in adoption, and a rich collection, may resolve this constraint. Based on its license, an OER can be reused, redistributed, revised, and remixed for different purposes. This study aimed to identify the instructional strategies for adopting OERs through reuse, redistribution, revision, and remix to reduce statistics anxiety. The formative design feature in this study can bridge the gap to combine the theoretical (i.e., instructional strategies to reduce statistics anxiety) and practical (i.e., OER adoption through reuse, redistribution, revision, and remix) aspects in teaching and learning. We also investigated the students’ perceptions of using OERs to reduce statistics anxiety. Our findings indicated that the use of OERs has had some positive effects in reducing statistics anxiety. In addition, the current study can serve as an example for instructors and instructional designers to learn how to rigorously adopt OERs through reuse, redistribution, revision, and remix in an interdisciplinary curriculum.

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. Amiel, T. (2013). Identifying barriers to the remix of translated open educational resources. International Review of Research in Open and Distance Learning, 14(1), 126–144.Google Scholar
  2. Bell, J. A. (2003). Statistics anxiety: the nontraditional student. Education, 124(1), 157–162.Google Scholar
  3. Bowen, W. G., Chingos, M. M., Lack, K. A., & Nygren, T. I. (2014). Interactive learning online at public universities: evidence from a six-campus randomized trial. Journal of Policy Analysis and Management, 33(1), 94–111.CrossRefGoogle Scholar
  4. Boyle, E. A., MacArthur, E. W., Connolly, T. M., Hainey, T., Manea, M., Kärki, A., & van Rosmalen, P. (2014). A narrative literature review of games, animations and simulations to teach research methods and statistics. Computers & Education, 74, 1–14. doi:10.1016/j.compedu.2014.01.004.CrossRefGoogle Scholar
  5. Brown, M. J., & Tallon, J. (2015). The effects of pre-lecture quizzes on test anxiety and performance in a statistics course. Education, 135(3), 346–350.Google Scholar
  6. Bui, N. H., & Alfaro, M. A. (2011). Statistics anxiety and science attitudes: age, gender, and ethnicity factors. College Student Journal, 45(3), 573–585.Google Scholar
  7. Carolan, J., & Guinn, A. (2007). Differentiation: lessons from master teachers. Educational Leadership, 64(5), 44–47.Google Scholar
  8. Caswell, T., Henson, S., Jensen, M., & Wiley, D. (2008). Open educational resources: enabling universal education. International Review of Research in Open and Distance Learning, 9(1), 1–12.Google Scholar
  9. Chance, B., Ben-Zvi, D., Garfield, J., & Medina, E. (2007). The role of technology in improving student learning of statistics. Technology Innovations in Statistics Education, 1(1), 1–24.Google Scholar
  10. Clements, K. I., & Pawlowski, J. M. (2012). User-oriented quality for OER: understanding teachers’ views on re-use, quality, and trust. Journal of Computer Assisted Learning, 28(1), 4–14.CrossRefGoogle Scholar
  11. Creswell, J. W., & Clark, V. L. P. (2007). Designing and conducting mixed methods research. Thousand Oaks: Sage.Google Scholar
  12. Cruise, R. J., Cash, R. W., & Bolton, D. L. (1985). Development and validation of an instrument to measure statistical anxiety, Paper presented at the American Statistical Association, Section on Statistical Education, Las Vegas.Google Scholar
  13. Cybinski, P., & Selvanathan, S. (2005). Learning experience and learning effectiveness in undergraduate statistics: modeling performance in traditional and flexible learning environments. Decision Sciences Journal of Innovative Education, 3(2), 251–271. doi:10.1111/j.1540-4609.2005.00069.x.CrossRefGoogle Scholar
  14. D'Antoni, S. (2009). Open educational resources: reviewing initiatives and issues. Open Learning, 24(1), 3–10.CrossRefGoogle Scholar
  15. Duncan, S. M. (2009). Patterns of learning object reuse in the Connexions repository. (Doctoral dissertation), Utah State University, Logan, UT.Google Scholar
  16. Dunn, K. (2014). Why wait? The influence of academic self-regulation, intrinsic motivation, and statistics anxiety on procrastination in online statistics. Innovative Higher Education, 39(1), 33–44. doi:10.1007/s10755-013-9256-1.CrossRefGoogle Scholar
  17. Dykeman, B. F. (2011). Statistics anxiety: antecedent and instructional interventions. Education, 132(2), 441–446.Google Scholar
  18. Fischer, L., Hilton III, J., Robinson, T. J., & Wiley, D. A. (2015). A multi-institutional study of the impact of open textbook adoption on the learning outcomes of post-secondary students. Journal of Computing in Higher Education, 27(3), 159–172.CrossRefGoogle Scholar
  19. Groeneboom, P., de Jong, P., Tischenko, D. B., & van Zomeren, B. C. (1996). Computer-assisted statistics education at Delft University of Technology. Journal of Computational and Graphical Statistics, 5(4), 386–399.Google Scholar
  20. Hanna, D., Shevlin, M., & Dempster, M. (2008). The structure of the statistics anxiety rating scale: a confirmatory factor analysis using UK psychology students. Personality & Individual Differences, 45(1), 68–74. doi:10.1016/j.paid.2008.02.021.CrossRefGoogle Scholar
  21. Hilton, J., Wiley, D. A., & Lutz, N. (2012). Examining the reuse of open textbooks. International Review of Research in Open and Distance Learning, 13(2), 45-58.Google Scholar
  22. Hilton, J., Wiley, D., Stein, J., & Johnson, A. (2010). The four ‘R’s of openness and ALMS analysis: frameworks for open educational resources. Open Learning, 25(1), 37-44.Google Scholar
  23. Hilton, J., Gaudet, D., Clark, P., Robinson, J., & Wiley, D. (2013). The adoption of open educational resources by one community college math department. International Review of Research in Open and Distance Learning, 14(4), 37–50.Google Scholar
  24. Hockings, C., Breet, P., & Terentjevs, M. (2012). Making a difference—inclusive learning and teaching in higher education through open educational resources. Distance Education, 33(2), 237–252. doi:10.1080/01587919.2012.692066.CrossRefGoogle Scholar
  25. Kimmons, R. (2015). OER quality and adaptation in K-12: comparing teacher evaluations of copyright-restricted, open, and open/adapted textbooks. The International Review of Research in Open and Distributed Learning, 16(5), 39–57. doi:10.19173/irrodl.v16i5.2341.CrossRefGoogle Scholar
  26. Kolar, D. W., & McBride, C. A. (2003). Creating problems to solve problems: an interactive teaching technique for statistics courses. Teaching of Psychology, 30(1), 67–68.Google Scholar
  27. Lalayants, M. (2012). Overcoming graduate students’ negative perceptions of statistics. Journal of Teaching in Social Work, 32(4), 356–375.CrossRefGoogle Scholar
  28. Lane, A., & McAndrew, P. (2010). Are open educational resources systematic or systemic change agents for teaching practice? British Journal of Educational Technology, 41(6), 952–962.CrossRefGoogle Scholar
  29. Lesser, L. M., & Reyes III, R. (2015). Student reactions to the integration of fun material in a high-anxiety subject: a case study in the teaching of college introductory statistics. Transformative Dialogues: Teaching & Learning Journal, 8(1), 1–19.Google Scholar
  30. Lloyd, S. A., & Robertson, C. L. (2012). Screencast tutorials enhance student learning of statistics. Teaching of Psychology, 39(1), 67–71.CrossRefGoogle Scholar
  31. Lovett, M., Meyer, O., & Thille, C. (2008). The open learning initiative: measuring the effectiveness of the OLI statistics course in accelerating student learning. Journal of Interactive Media in Education, 14, 1-16. doi:10.5334/2008-14 Retrieved from https://eric.ed.gov/?id=EJ840810.
  32. 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.CrossRefGoogle Scholar
  33. Macher, D., Paechter, M., Papousek, I., Ruggeri, K., Freudenthaler, H. H., & Arendasy, M. (2013). Statistics anxiety, state anxiety during an examination, and academic achievement. British Journal of Educational Psychology, 83(4), 535–549. doi:10.1111/j.2044-8279.2012.02081.x.CrossRefGoogle Scholar
  34. Manalo, E., & Leader, D. (2007). Learning center and statistics department collaboration in improving student performance in introductory statistics. College Student Journal, 41(2), 454–459.Google Scholar
  35. McGrath, A. L. (2014). Content, affective, and behavioral challenges to learning: students’ experiences learning statistics. International Journal for the Scholarship of Teaching and Learning, 8(2), 1–21.CrossRefGoogle Scholar
  36. Miller, S. E. (2010). Literacy coaching and teachers’ instructional practices: the impact of the community coaching cohort model. (Doctoral dissertation), College of William and Mary, Ann Arbor, MI. Retrieved from ERIC database.Google Scholar
  37. Mills, J. D. (2004). Students’ attitudes toward statistics: implications for the future. College Student Journal, 38(3), 349–361.Google Scholar
  38. Murtonen, M., Olkinuora, E., Tynjälä, P., & Lehtinen, E. (2008). ’Do I need research skills in working life?’: university students’ motivation and difficulties in quantitative methods courses. Higher Education, 56(5), 599–612.Google Scholar
  39. Neumann, D. L., Neumann, M. M., & Hood, M. (2011). Evaluating computer-based simulations, multimedia and animations that help integrate blended learning with lectures in first year statistics. Australasian Journal of Educational Technology, 27(2), 274–289.CrossRefGoogle Scholar
  40. Neumann, D. L., Hood, M., & Neumann, M. M. (2012). An evaluation of computer-based interactive simulations in the assessment of statistical concepts. International Journal for Technology in Mathematics Education, 19(1), 17–24.Google Scholar
  41. Olcott Jr., D. (2012). OER perspectives: emerging issues for universities. Distance Education, 33(2), 283–291. doi:10.1080/01587919.2012.700561.CrossRefGoogle Scholar
  42. Onwuegbuzie, A. J. (2000). Statistics anxiety and the role of self-perceptions. The Journal of Educational Research, 93(5), 323–330.CrossRefGoogle Scholar
  43. Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics anxiety: nature, etiology, antecedents, effects, and treatments—a comprehensive review of the literature. Teaching in Higher Education, 8(2), 195–209.CrossRefGoogle Scholar
  44. Onwuegbuzie, A. J., Leech, N. L., Murtonen, M., & Tähtinen, J. (2010). Utilizing mixed methods in teaching environments to reduce statistics anxiety. International Journal of Multiple Research Approaches, 4(1), 28–39. doi:10.5172/mra.2010.4.1.028.CrossRefGoogle Scholar
  45. Pan, W., & Tang, M. (2004). Examining the effectiveness of innovative instructional methods on reducing statistics anxiety for graduate students in the social sciences. Journal of Instructional Psychology, 31(2), 149–159.Google Scholar
  46. Pan, W., & Tang, M. (2005). Students’ perceptions on factors of statistics anxiety and instructional strategies. Journal of Instructional Psychology, 32(3), 205–214.Google Scholar
  47. Petrides, L., Nguyen, L., Jimes, C., & Karaglani, A. (2008). Open educational resources: inquiring into author use and reuse. International Journal of Technology Enhanced Learning, 1(1–2), 98–117.CrossRefGoogle Scholar
  48. Rabin, L. A., & Nutter-Upham, K. E. (2010). Introduction of a journal excerpt activity improves undergraduate students’ performance in statistics. College Teaching, 58(4), 156–160. doi:10.1080/87567555.2010.484034.CrossRefGoogle Scholar
  49. Rogerson-Revell, P. (2007). Directions in e-learning tools and technologies and their relevance to online distance language education. Open Learning, 22(1), 57–74.CrossRefGoogle Scholar
  50. Sciutto, M. J. (2002). The methods and statistics portfolio: a resource for the introductory course and beyond. Teaching of Psychology, 29(3), 213–215.CrossRefGoogle Scholar
  51. UNESCO (2002). Forum on the impact of open courseware for high education in developing countries: final report. Retrieved from www.open.ac.uk/about/ou/p2.shtml.
  52. Vigil-Colet, A., Lorenzo-Seva, U., & Condon, L. (2008). Development and validation of the Statistical Anxiety Scale. Psicothema, 20(1), 174–180.Google Scholar
  53. Wiley, D., & Gurrell, S. (2009). Context and catalyst: a decade of development. Open Learning, 24(1), 11–21.CrossRefGoogle Scholar
  54. Wiley, D., Bliss, T. J., & McEwen, M. (2014). Open educational resources: a review of the literature. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (pp. 781–789). New York: Springer.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications & Technology 2017

Authors and Affiliations

  1. 1.Center for Excellence in Teaching & LearningGeorgia State UniversityAtlantaUSA
  2. 2.Department of Learning, Performance, and SystemPennsylvania State UniversityUniversity ParkUSA

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