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This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

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Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (

Generally, academic psychologists are aware of students’ perceptions regarding the “dull, difficult, and distressing” nature of research methods and statistics (

The aim of the present paper is to discuss critically how the use of eLearning systems may facilitate the engagement of psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we discuss numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts. Finally, we examine general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

Numerous scholars define eLearning as a variety of online technologies (e.g.,

However, the aforementioned definition is problematic for a number of reasons. First, this definition assumes

Pedagogy 2.0 is similar, at least in part, to

Presence pedagogy, with its focus on interaction as a principal method of

Knowledge creation not only further enhances individual knowledge, but “advance[s] community knowledge as a public product” (

According to

In order for knowledge creation to be actualized as a new pedagogical strategy, instructional design must develop into “a more innovation-oriented approach” (

In order to promote a knowledge creating community, a collaborative assessment task could be developed in which students work together to deepen their understanding of the statistical notion “

A

We are mindful that previous research demonstrates that humorous teaching strategies may reduce students’ statistics anxiety and promote positive affect (e.g., happiness; e.g.,

In another class demonstration devised by one of us, the objective is to explicate an inferential statistical test referred to as a Pearson’s product-moment correlation, which measures the strength of the relationship between two variables. To illustrate the concept of a correlation, one of us invokes the character “Barney” from the American situational-comedy “How I Met Your Mother.” The episode in which Barney is outlining the relationship between being hot (i.e., aesthetically pleasing) and crazy is described. As a class, we discuss that Barney is arguing that: (1) the correlation is high (i.e., strong); and (2) the direction of the relationship is positive (i.e., as hotness increases so too does craziness). At this point in the proceedings, students often like to venture anecdotes of their own past romantic relationships with hot and crazy individuals.

Importantly, the aforementioned class demonstrations are typically delivered in an eLearning context, using, specifically,

In addition, the content-area of statistics is hierarchical. For example, analysis of variance is an extension of the

In contrast, the online discussion forums of eLearning systems provide an opportunity for students to co-create, and traverse, rhizomatic pedagogical cyberspace. For example, as previously stated, in one of our research methods and statistics eLearning systems, students have used online discussion forums to create memes using popular cultural references (e.g., Chuck Norris) with the aim of elucidating statistical concepts. Each popular cultural reference may be conceptualized as a point of a decentered system, which may connect with other points in a multitude of ways without recourse to order or hierarchy (

The notion of escape routes from striated space is reminiscent of

An example that one of us devised with the aim of creating a line of flight within an eLearning system is concerned with the ontology of numbers. The teacher pours a carton of milk into a saucer, writes a cat’s name (e.g., “Felix”) on a slip of paper and, subsequently, places the paper in the saucer. The teacher says to the class: “Felix initially appeared quite dehydrated but now he seems replenished!” Students invariably laugh and the teacher asks what is humorous about this scenario. The students explain that writing a cat’s name on a piece of paper does not constitute a real cat. The teacher responds, “Yes!” The teacher suggests that the linguistic term (i.e., word) “cat” is a signifier that is referentially linked to an object (i.e., the signified) in the external world with whiskers, fur, a tail and a tendency to “meow.” In addition, the teacher asserts that:

Feeding milk to a linguistic term is an example of confusing the signifier with the signified. It would seem to follow that I have never seen a number and, in fact, do not know what a number is. Why? If I were to write, for example, “8” on the board, then this would constitute a symbol (i.e., the signifier) that is referentially linked to a number (i.e., the signified). However, to assert that “8” is a number is to confuse the signifier with the signified just like I confused the slip of paper with “Felix” written on it with the physical object in the external world.

This demonstration may be delivered via web-conferencing tools (e.g., Adobe Connect) and creates a line of flight by encouraging students to reflect critically on the nature, essence, and existence of numbers and, thus, statistics.

As previously stated, traditional eLearning is often reducible to a “network of static hypertext pages” (

The innovative potential of VWs provides an opportunity to reshape pedagogical approaches rather than merely replicate traditional teaching methods (

In comparison with the 2-D web, VWs provide numerous innovative ways to facilitate learning (

In addition, VWs may be used to provide virtual field trips (VFTs), which, via web technologies, can simulate the experience of fieldwork (

Below are two practical examples of statistical methods that can be taught in an engaging and novel way using VWs. We chose VWs because previous research using a VW to engage students in research methods has shown promising results in improving student knowledge and confidence (

Factor analysis is a statistical method used to reduce a large number of variables to a smaller set that best capture the information in the original set. Variables that correlate highly are coalesced into one factor. If multiple factors emerge, then they are structured so as to be largely independent of one another (

The following is a student demonstration designed to provide a rudimentary introduction to the concept of factor analysis in the context of

The teacher avatar (hereafter “teacher”) invites 15 to 20 student avatars (hereafter “students”) to stand at the front of the virtual class.

The students are informed they each represent separate variables concerning hair color and, for simplicity, we are interested in the extent to which each variable (or student) correlates with the broader shades of either blonde or dark hair color.

The teacher explains the goal of the demonstration is to reduce the number of variables from 20 to perhaps two or three.

The teacher invites all students with blonde hair to stand together and all students with dark hair to stand together. In so doing, the factor analysis has derived just two factors (i.e., blonde and dark hair), and by using just these two factors the analysis captures a substantial amount of the variation in hair color that was present in the original 20 students. Clearly, having just two factors (or variables) is far more parsimonious than 20.

There will be generally students with brown hair; these students were ignored until now. The teacher then asks whether these students should: (a) be combined with the blonde hair group to create a single blonde-brown group; (b) be combined with the dark hair group to create a single dark-brown group; or (c) form their own group. This allows the teacher to consider what number of factors would be ideal, two or three? The issue is central to factor analysis. For the sake of this demonstration, three factors may be selected.

Typically, there is a student with red or gray hair in the virtual class. The teacher invites these students to walk to the front of the virtual class and join the group to which they belong. However, these students will fit into none of the existing groups. The teacher points out that these students represent an outlier at the variable level. These students are, accordingly, removed from the factor analysis and asked to sit down.

The teacher explains that a factor is a composite of individual variables which all measure the same latent construct. In this example, we have an

Finally, the teacher explains that these factors are used as predictors in subsequent analyses (e.g., predicting the dependent variable, ethnicity).

Discriminant Function Analysis (DFA) is a statistical method used to predict membership on a categorical (i.e., grouping) dependent variable (DV) from one or more continuous or binary independent variables (IVs). DFA is used when groups are known

The following is a class activity designed to provide an illustration of DFA in the context of

The teacher avatar (hereafter “teacher”) invites 15–30 student avatars (hereafter “students”) to line up in a virtual open space. (One may use between one and three lines depending on the number of students and the size of the virtual space.)

For each line, the teacher nominates a student to be the DFA method “in action.”

The teacher invites the nominated students to try and predict, for each student in their line, if each student’s father has dark, blonde, or no hair. Fathers’ hair type is the DV (i.e., grouping variable).

The teacher explains that the predictions are based on multiple continuous IVs, which include each student’s hair color, complexion, and number of hair follicles. Clearly, not all predictions will be correct, which provides a useful illustration of the potential (in) accuracy of the model.

The students are invited to stand in one of three groups that represent whether their father has (or had): (a) dark hair; (b) blonde hair; or (c) is bald. The location of each group is illustrated in

Subsequently, the teacher explains that the angle of the first discriminant function, as shown in

The teacher explains that the second discriminant function, as shown in

The teacher highlights that the two discriminant functions are orthogonal to each other. If there were a third discriminant function it would point directly up in the air.

The teacher reports that the scores on the discriminant functions represent standardized

The teacher provides an example of what a standardized value on, for example, the first discriminant function represents. That is, if a student had a high score (i.e., greater than zero) on the first discriminant function they would be in the dark haired group. Importantly, however, if a student had a low score on this first discriminant function they could be in either the blonde or bald group. The teacher explains that it is only by also looking at the student’s score on the second discriminant function that we can discern which group they belong to. If a student had a low (i.e., less than zero), rather than high, score on the second discriminant function, and a low score on the first, they would be in the blonde group.

Thus far, we have only attempted to predict group membership. When creating our model, we also need to assess the accuracy of the model by comparing our predictions against the true, rather than predicted, status. We can acquire this information by simply asking each student if his or her father is dark, blonde or bald.

The teacher invites the students who were incorrectly classified to sit down.

Within one group, the teacher explains that the students standing represent the accurate classifications of that group, which can be converted to the percentage correct. This step may be repeated for each of the remaining groups.

Subsequently, the previous step is repeated for all students participating in the demonstration (i.e., the analysis). Thus, the students standing represent the total number of accurate classifications, which can be converted to the total percentage correct.

Finally, the teacher states that DFA models are created with data where the true, rather than predicted, status is known. The goal of DFA is to use the model to generalize beyond the sample in order to predict group status for cases where the true state is unknown. To illustrate this point, the teacher pretends they are an orphan and do not know, or will ever know, their father’s identity. However, it is possible to use the DFA model to predict what group the teacher will fall into and, thus, the teacher’s father’s hair color.

Mobile devices and the social web are the most important eLearning tools for the near future; and

Games should be deemed an important eLearning tool.

These findings are supported by numerous studies (e.g.,

Trend (1) refers to the current shift from eLearning to mobile learning (mLearning), which involves “the use of mobile or wireless devices for the purpose of learning while on the move” (

Trend (2) refers to the realization by eLearning providers that video game technology can be used to develop fun and immersive simulations (

The objective of the present paper was to examine critically how teachers seeking to engage psychology students in research methods and statistics might use eLearning systems. We demonstrated how various eLearning-related pedagogical principles (i.e., Pedagogy 2.0, Presence Pedagogy, learning as knowledge creation, a pedagogy of desire, striated space versus rhizomatic space, lines of flight) might be applied in the context of teaching research methods and statistics, using examples from our own teaching. Subsequently, we devised two practical examples concerning how Virtual Worlds (e.g.,

In the current era of academic capitalism, which is characterized by the emergence of the entrepreneurial, online university, we note that teachers are constrained to engage in market-like behavior (

All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.