Time has been argued by several influential approaches as essential for understanding learning and teaching processes. In e-learning, however, the traditional time limits of such processes are modified, which implies challenges and possibilities for e-learning research. This paper is aimed at understanding how time is included in empirical e-learning research literature. With this aim in mind, the paper presents a qualitative review of 24 e-learning papers. Five issues are analysed: the conception of time, its inclusion in an explicative model, its inclusion in the research process, the analytical units, and the data used in the study of time. Based on our analysis, we discuss some implications and potentialities for e-learning research on the relations between time and learning.
Time is such a ubiquitous factor of all human activity that sometimes it becomes invisible. This also happens with other omnipresent phenomena such as the atmosphere: sometimes we do not realise it is there; however, the atmosphere is crucial for understanding the Earth’s physical phenomena. Similarly, time is absolutely crucial for understanding human activity, and especially, for comprehending educational phenomena.
The inclusion of time in the conceptualisation of educational phenomena is not something new. Ebbinhause, for example, already observed in 1885 what was called the “spacing effect” [
A second highly influential conceptualisation of learning in relation to time was proposed by Skinner, who constructed a development of behaviourism which was called operant conditioning [
A third important view that considers time from the very essence of educational phenomena is mainly represented by two exceptionally influential approaches to learning and development: the proposals of Piaget and Vygotsky (see, e.g., [
The transformation of time limits, therefore, is one of the consequences of the irruption of digital technologies in educational processes. According to Harasim ([
So, from this starting point, what we have in mind is an opportunity to investigate the relationship between time and learning by considering time conditions, which were not possible before the advent of digital technologies. As we see it, this is an opportunity to push forward what we already know about this relationship, which is conceptualised differently depending on the theoretical and research tradition (we have briefly outlined the three main traditions above). The endeavour, therefore, implies
This is the broad aim we have in mind when writing the present paper. However, in this paper, we only attempt to take an initial small step in order to make this endeavour possible. The argument is that,
This paper aims to contribute to addressing some methodological problems of the inclusion of time in empirical e-learning research by offering a picture of how it is done in empirical literature in this field.
We believe that this picture represents a very first step towards making a broader endeavour possible, one which is able to push forward our understanding of the relation between time and learning by investigating this issue under new time conditions.
According to this proposal, the organisation of this paper will proceed as follows: after presenting the method of this study, in Section
The selection of papers was carried out in three phases. In the first phase, framework phase, we used the ERIC database (Education Resources Information Center) to identify current papers related to e-learning published during 2006–2009. ERIC was used because of its significance in the educational field [
In the second phase—time-centred phase—we refined the search by introducing several time descriptors in order to select those papers which considered time in any way. At that point, we looked for several broad temporal key words—such as time, pace, rhythm, phase, timeless, speed, and schedule, as well as progression, sequence, duration, and in the title, abstract or descriptors of papers. This selection process was complex mainly because of the contextual and polysemic nature of terms related with time. Sometimes those terms were used in a general way without contributing any different meanings to the research questions and units (“from a long time ago,” “it is no time for,” etc.). Furthermore, on other occasions, the terms were related to a global description needed in one part of the paper (e.g., in a data gathering explanation: a ten-week course). We also took a general decision about papers that include terminology regarding synchrony-asynchrony: we rejected the papers which used these terms only as a global framework or as a common label without addressing the implications of synchronic or asynchronic communication for learning (e.g., collaborative learning in asynchronous environments, synchronous communication, amongst others). By doing so, we reduced the number to 878 papers, but the results were still too broad. Consequently, specific criteria were then applied by examining the titles and abstracts of the papers. We admitted explicitness and implicitness of the consideration of the time dimension. Specifically, for implicitness the following criteria were applied: when a central dimension of study that implies the consideration of time is used. when a methodological technique that includes the consideration of time is used. when the study includes the analysis of a technological tool that includes time in its functioning (e.g., a real-time simulator), or that changes the time conditions of the task (e.g., an asynchronous communication tool).
Using this procedure yielded 173 papers. At this point we began the third phase, time approached phase, in which we introduced more specific criteria regarding how the papers had to consider the time factor. We considered only papers that report an empirical study. We considered only papers that include time in the methodological infrastructure as
After this last screening, we were left with 24 papers. These criteria were chosen because our purpose was to know more about the inclusion of time in research, not only in theory or instructional and technological practice. For this reason we needed to work with empirical papers, which actually consider time empirically—that is, in the analysis of results; in Section
Once the 24 papers had been selected, we qualitatively analysed them by open coding according to grounded theory analytic logic [ How is time conceptualised in relation to teaching and learning phenomena? How is time introduced into a theoretical explicative model of online teaching and learning? How is time operationalised and introduced into a research process? Which kinds of units are used for this operationalisation of time? Which types of data are useful for studying time?
From these five generative questions, we examined the data and established tentative categories, which were continually verified and reformulated in dialectic relation with the data. According to Grounded Theory logic, the establishment of these categories was fully data driven. In doing so, we continually compared the options (indicators) taken by the different papers in order to relate them to the constructed categories and at the same time we reformulated the categories in order to fit them to their correspondent indicators in the data. This procedure was carried out until we obtained a consistent system of categories responding to each generative question; that is, all of the specific responses in the data were properly conceptually gathered together by the constructed categories. This analysis was carried out by means of three interrelated and constant procedures: coding, memoing, and data collecting. Coding means constructing categories that respond to the observations in data; memoing means writing iterative interpretations about categories; data collecting means, in our specific analysis, iteratively considering different parts of data for coding and memoing. These three procedures dialectically involve each other: the data usually challenged our tentatively constructed categories and led us to modify coding or to add a category, this at the same time led us to complete or rethink previous interpretations of data, which could also lead us to again modify coding, and which in turn could lead us to contrast with other parts of data, and so on.
We will organise the results according to the five guiding questions that we proposed in section
Answers found in the review for the five guiding questions.
Question | Answers found in the review | # of papers ( | % of papers |
---|---|---|---|
Conceptualisation of time | Time as the while during which a phenomenon is taking place | 10 | 41.7% |
Time as the moment in which a phenomenon takes place | 3 | 12.5% | |
Time as the temporal distance between two phenomena | 8 | 33.3% | |
Time as the evolution of a phenomenon | 7 | 29.2% | |
Introduction of time into an explicative model | Variable-based model | 20 | 83.3% |
Model of a process | 4 | 16.7% | |
Introduction of time into the research process | Introduction into the phase of defining dimensions and variables | 23 | 95.8% |
Introduction into the analytic phase | 3 | 12.5% | |
Units used for analysing and measuring time | Units based on formal time | 15 | 62.5% |
Units based on internal components of the setting | 10 | 41.7% | |
Theoretical-based units | 3 | 12.5% | |
Data used for studying time | Electronic log files | 12 | 50% |
Questionnaires, surveys, and interviews | 6 | 25% | |
Learning process students’ products | 1 | 4.2% | |
Institutional documents | 1 | 4.2% | |
Setting design | 10 | 41.7% |
The categories in each question are not exclusive (indeed they are only exclusive in the second question); that is, one and the same approach can include, for example, two different ideas of time, can introduce time into both phases, can simultaneously use units of different nature, and can combine different kinds of data. For this reason, in each question, the frequencies of each of the categories do not total 24, and the percentages do not total 100.
Characterisation of literature according to the five guiding questions.
Meaning of time | Methodological incorporation of time | ||||
Theoretical model | Conception of time | Phase of incorporation | Unit | Data | |
Amiel and Orey [ | Variable-based model | While during which a phenomenon is taking place | Dimensions | Formal time unit | Questionnaire-surveys-interviews |
Bannink and van Dam [ | Process model | Evolution of a phenomenon | Dimensions | Theoretical-based unit | Log filesProducts |
Y. Chen et al. [ | Process model | Evolution of a phenomenon | Analysis | Formal time unit | Log files |
D. T. Chen et al. [ | Variable-based model | While during which a phenomenon is taking place | Dimensions | Not specified | Setting design |
Cotner et al. 12] | Variable-based model | Distance between two phenomena | Dimensions | Unit based on internal components of the setting | Setting design |
Evolution of aphenomenon | Analysis | Formal time unit | Questionnaire-surveys-interviews | ||
Crooks et al. [ | Variable-based model | Distance between two phenomena | Dimensions | Unit based on internal components of the setting | Setting design |
While during which a phenomenon is taking place | Dimensions | Formal time unit | Log files | ||
Ely et al. [ | Variable-based model | While during which a phenomenon is taking place | Dimensions | Formal time unit | Setting design |
Frydenberg [ | Variable-based model | Moment in which the phenomenon takes place | Dimensions | Unit based on internal components of the setting | Institutional documents |
Evolution of a phenomenon | Analysis | Formal time unit | |||
Hrastinski [ | Variable-based model | Distance between two phenomena | Dimensions | Unit based on internal components of the setting | Setting design |
While during which a phenomenon is taking place | Dimensions | Formal time unit | Questionnaire-surveys-interviews | ||
Hrastinski [ | Variable-based model | Distance between two phenomena | Dimensions | Unit based on internal components of the setting | Setting design |
Hrastinski [ | Variable-based model | Distance between two phenomena | Dimensions | Unit based on internal components of the setting | Setting design |
Jeong and Frazier [ | Variable-based model | Moment in which the phenomenon takes place | Dimensions | Formal time unit | Log files |
Kapur et al. [ | Variable based model | Evolution of a phenomenon | Dimensions | Unit based on internal components of the setting | Log files |
Kim and Keller [ | Variable-based model | While during which a phenomenon is taking place | Dimensions | Formal time unit | Questionnaire-surveys-interviews |
Kevin et al. [ | Variable-based model | While during which a phenomenon is taking place | Dimensions | Formal time unit | Log files |
Krause et al. [ | Variable-based model | While during which a phenomenon is taking place | Dimensions | Formal time unit | Log files |
Martin and Klein [ | Variable-based model | While during which a phenomenon is taking place | Dimensions | Formal time unit | Log files |
Metz [ | Variable-based model | Moment in which the phenomenon takes place | Dimensions | Formal time unit | Log files |
Offir et al. [ | Variable-based model | Distance between two phenomena | Dimensions | Unit based on internal components of the setting | Setting design |
Osman and Herring [ | Process model | Evolution of a phenomenon | Dimensions | Theoretical-based unit | Log files |
Papadopoulos et al. [ | Variable-based model | While during which a phenomenon is taking place | Dimensions | Formal time unit | Log files |
Roblyer et al. [ | Variable-based model | Distance between two phenomena | Dimensions | Unit based on internal components of the setting | Setting design |
Skylar [ | Variable-based model | Distance between two phenomena | Dimensions | Unit based on internal components of the setting | Setting design |
Yoon and Johnson [ | Process model | Evolution of a phenomenon | Dimensions | Theoretical-based unit | Log files |
Questionnaire-surveys-interviews |
Time can be used in literature with several different meanings, and it is crucial to distinguish between them in order to know what is actually being studied behind the label of “time.” In our review, we have basically found four different ideas of time. First of all, in 10 of the 24 studies (see Tables
Another conception of time used in the reviewed literature (3 studies out of 24) is the understanding of time as
A third conception of time we have found widely in the data (8 studies out of 24) is the understanding of time as
The forth conception of time found in our study (7 studies out of 24) is the understanding of time as
The four conceptions of time identified above can be included in an explicative model in two different basic ways: in a variable-based model or in a model of a process. When time is introduced into a variable-based model, it is taken as a variable, that is, it is isolated from the whole environment and relations with other isolated variables are sought. For example, Ely et al. [
Nevertheless, time can also be introduced into a model not based on variables, but rather into a model which tries to explain a process [
In our data, the majority of studies, as many as 20 out of 24, adopted a variable-based model, while only 4 studies adopted a model of a process.
Once we become aware of the conception of time and of how this conception is integrated into an explicative model, it is then necessary to understand how it can be translated into the actual research process. According to the reviewed literature, this incorporation of time into research can take place in two different phases of the process: the phase of establishing and defining dimensions and variables and the phase of analysis.
The incorporation of time via
From a process model, this operationalisation is done using dimensions of analysis, which take the time conception from the explicative model. For example, Osman and Herring [
However, some approaches do not incorporate time via dimensions or variables, but only in the
Several conceptions of time can be incorporated by the same approach, and this can be done in both phases of the research process. For example, in Cotner et al. [
Once we have seen how time is incorporated into the research process, the question that then arises is how time is measured. In our paper, we have found three kinds of units, which permit time to be measured and analysed: units based on formal time, units based on internal components of the setting, and units based on theoretical models.
By
Another option is to use
Finally, several approaches use
In the studies reviewed, the most commonly used units are units based on formal time (15 studies) and units based on internal components of the setting (10 studies), which in some instances (4 studies) are combined—see Table
After examining the units and measurements used for studying time, what remains unaddressed is what type of data is useful for measuring and analysing time. In our paper, we have basically found five types of data which different approaches use for doing so: electronic log files, questionnaires, surveys, interviews, learning process products, institutional documents, and the setting design.
Other approaches use data produced in the direct interaction between the researcher and the participants by means of
Another kind of data which can also be useful if properly timely situated in the process consists of the
Other data which can be useful in some specific approaches consists of
Finally, some measures of time are not taken from data gathered with this aim, but just experimentally adopted as part of the
Obviously, these different types of data can be combined for measuring time in the same approach. For example, [
The studies included in this revision mainly used log files (12 studies) and the setting design (10 studies) as data for analysing time, followed by questionnaires-surveys-interviews (6 studies). Setting design and log files are combined in one study [
As stated in the first section, in writing this paper we had two aims in mind. The immediate problem this paper addresses is the methodological inclusion of time in empirical e-learning research. However, in our approach, this methodological problem must be seen as a part of a broader aim, namely, advancing our understanding of the relations between time and learning by doing research on this issue with new time conditions that permit the enrichment and development of existing conceptualisations of such relationships. In the last section, we presented a picture of how the selected empirical literature on e-learning solves the immediate methodological problem of the inclusion of time in e-learning research. In this discussion, we will examine how this picture has implications for our more general aim and, therefore, in which directions it should be developed towards this aim.
From this point of view, the first relevant aspect of our results is that the methodological problem of including time in empirical e-learning research is addressed by a small proportion of the literature we examined. It is necessary to be very cautious on this point because of the limitations of our paper selection procedure. However, according to our operationalisation, we only found 24 papers that empirically considered time in their analysis. No further discussion is needed on this point: obviously, if time is not included in empirical research, no empirical data will be available on the relations between time and learning, so the existing conceptualisations about time and learning will hardly be challenged and enriched.
Another relevant issue of the picture drawn in the last section, from the point of view of our broader aim, is the connection—or lack of connection—between the empirical approaches which include time and any well-articulated previous conceptualisation of the relations between time and learning. As we mentioned in the first section, the theoretical postulates about the relations between time and learning were mainly implicit in the papers that we reviewed. However, the connection could also be implicit. In our analysis, we identified four conceptions of time, and if we examine the implicit connection between these conceptions and the existing main conceptualisations of the relations between time and learning, we could say that implicit connections can be seen but only on a superficial level. For example, studies that consider “time as the while during which a phenomenon is taking place” address the relation between time and learning by means of the expression of learning (or variables involved in learning) in terms of time, which, as we saw, is essentially the same basic operation that lies behind Carroll’s model and its developments. This, however, does not mean that these studies assume the other aspects of Carroll’s model. Something similar happens with the conception of “time as the evolution of a phenomenon”, in which there is the idea that learning (or its variables or dimensions) is a phenomenon that changes and evolves over time; the same general idea as in the genetic approaches. Again, however, the studies cannot be associated to a specific articulation of time and learning, but only to this general genetic idea. In the studies that consider the conception of “time as the moment in which a phenomenon takes place,” there is the idea that the effect of an action or phenomenon and its implications on learning (or its variables) varies according to the moment in which this action or phenomenon takes place. This idea is somewhat similar to Skinner’s emphasis on the specific moment in which acts and reinforcements take place. However, the other aspects of Skinner’s conceptualisation are not taken on board by such studies.
If there is not a substantive connection, even an implicit one, between empirical research and any well-articulated conceptualisation of the relations between time and learning, the reconstruction, challenging, and enrichment of the understanding of these relations will hardly be possible, because it will be very difficult to situate the empirical results theoretically. According to our analysis, these connections are not substantive, but only superficial. This fact, in our view, seriously hinders improving our understanding of the relations between time and learning.
A third relevant issue uncovered by our results is the presence or absence, in the methodological incorporation of time in current empirical research on e-learning, of ideas, methods, or resources which are potentially capable of challenging and enriching the existing understanding of the relations between time and learning. According to our analysis, these potentialities do exist in empirical research. The first potentially challenging-enriching idea is the broad incorporation into empirical research of the conception of “time as the temporal distance between two phenomena.” In this conception, there is the idea that the rhythm of different phenomena, and especially the rhythm of interaction, has implications for learning (or its variables). The growing interest in this idea is clearly related to the mediation of digital technologies, which permit a great range of interaction rhythms between face-to-face synchronicity and traditional epistolary asynchronicity. As far as we know, the relationship between the different possible rhythms of interaction (or other phenomena) and learning (or their variables) is not yet conceptualised in any articulated learning theory. A second potentially enriching issue is the use of the conception of “time as the moment in which a phenomenon takes place” in e-learning time limits. This idea, as we saw, is superficially related to Skinner’s conceptualisation. When this conception is used in e-learning time limits, the “possible moments” are expanded to 24 h a day, 7 days a week. If this idea in empirical e-learning research was more substantially connected to Skinner’s proposals, or its later developments, the e-learning expansion of time limits could clearly enrich Skinner’s conceptualisation. A third potentially enriching resource we find in empirical e-learning research is the possibility of predesigning the temporal conditions of interactions without creating an artificial setting. This can permit great flexibility in testing or empirically developing theoretical ideas about the relations between time and learning. A fourth potentially enriching resource is the use of log files as data for analysis, since they allow the whole online interaction
Despite the contributions the present study may have made, it does also have some limitations which must be kept in mind. Although the selection procedure has been systematic, and based on specific criteria, the paper is obviously not complete. We based the study on the ERIC database because of its significance in the educational field, but of course there are many others and some approaches may have been missed. Nevertheless, we believe that the first small step we have attempted to take in this paper is an important one, because it is necessary in order to take the following steps towards a better understanding of the relation between time and learning.