Frontline Learning Research 6 (2014) 15-24
ISSN 2295-3159
Corresponding author: I.Molenaar@pwo.ru.nl
Doi: http://dx.doi.org/10.14786/flr.v2i4.118
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Advances in temporal analysis in learning and instruction
Inge Molenaar
a
a
Radboud University Nijmegen
Article received 8 June 2014 / revised 23 September 2014 / accepted 23 September 2014 / available online 23 December 2014
Abstract
This paper focuses on a trend to analyse temporal characteristics of constructs important to
learning and instruction. Different researchers have indicated that we should pay more attention to
time in our research to enhance explanatory power and increase validity. Constructs formerly
viewed as personal traits, such as self-regulated learning and motivation, are now conceptualized
as a series of events that unfold over time. This raises new questions with regard to the temporal
characteristics of these constructs and their dynamic interplay with learner and context
characteristics. Even though the value of analyzing temporal characteristics is becoming evident, a
number of challenges need to be tackled in order to make progress in the field of learning and
instruction. First, we need to be aware of the paradigm shift that temporal analysis entails. Second,
a common understanding of different dimensions of time and the position of temporal
characteristics therein can facilitate our time-related research dialogue. Third, a better
understanding how to answer time-related questions with appropriate methodological approaches
needs to emerge. Fourth, researching temporal characteristics requires procedures and guidelines
for segmenting time units. Fifth, temporal data are mostly collected at the micro level, whereas
most theory is defined at a macro level; consequently we need to bridge these differences in the
granularity used between collecting, coding and theorizing to enhance meaning making. Finally,
so far, most examples of time-related research are exploratory or comparative studies; the next step
is to move toward confirmative studies, which constitute the “Holy Grail” of temporal analysis.
Keywords: Temporal Analysis; Learning and Instruction; Time; Methodologies
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1. Introduction
Learning is defined as the acquisition of skills and knowledge and can be recognized through
changes in the learners’ behaviour (Mayer 2008; Zimmerman 2002). The concept of time is innate to
learning, as it takes time to acquire skills and knowledge and to signal changes in behaviour. In learning and
instruction research, we mostly capture time in pre- and post-test designs. As such we often focus on a
narrow concept of time, reducing the temporal characteristics of learning to the changes between pre- and
post-tests which reduces validity and explanatory power of our research. Currently, technological
advancements increase our ability to gain traces of learners while they are learning, which is an important
facilitator to overcome this limited focus on time in our field (Greene & Azevedo, 2010; Reimann, 2009;
Winne, 2010). A steadily growing group of researchers is raising questions that address how different
constructs act and develop over time (Bannert et al., 2014, Greene & Azevedo, 2010; Molenaar & Chiu,
2014; Riemann, 2009; Schmitz, 2006; Wise & Chiu, 2011). With this growing interest in temporal
characteristics of constructs at the heart of learning and instruction research, the need for temporal analysis is
becoming more prevalent.
This paper focuses on the developing trend in learning and instruction research to analyze temporal
characteristics of different constructs. The rationale for temporal analysis in our field is discussed as well as
the fact that temporal analysis entails a deviation from our main research approach, changing our analysis
from characteristics of students to attributes of. learning activities (Riemann, 2009; Schmidt, 2006).
Researchers have conceptualized temporal characteristics of learning and instruction constructs in many
ways in their research, leading to a diverse set of dimensions of time driving research questions. It is argued
that a conceptual framework of temporal characteristics can support transparency and enhance comparability
in the field. Lastly, a number of challenges are discussed that we, as a field, need to overcome to successfully
engage in temporal analysis.
2. The rationale for temporal analysis
A number of researchers in the field of computer-supported learning (Kapur, 2011; Reiman, 2009;)
and self-regulated learning (Greene & Azevedo, 2010; Schmitz, 2006; Schoor & Bannert, 2012) indicate that
we should pay more attention to time in the learning process. Existing research methods do not “fully” utilize
the temporal information embedded in the data collected (Kapur, Voiklis & Kinzer, 2008; Wise, Perera,
Hsiao, Speer & Marbouti, 2012). This reduces the explanatory power of the analysis performed and limits
the validity of the conclusions drawn (Akhras & Self, 2000; Reimann, 2009). For example, Kuvalja and
colleagues (2014) show that self-directed speech and self-regulatory behaviour of children with a specific
language impairment does not differ in frequency; neither the number of self-directed speech and self-
regulatory events during learning, nor the order between the these events as detected by sequential lag
analysis differed, but there was a difference between the two groups in the co-occurrence of self-directed
speech and self-regulatory behaviour as detected by temporal patterns analysis (Magnusson, 2000). This
indicates that without proper temporal analysis, existing differences between groups of learners cannot be
detected.
Moreover, a number of constructs, such as self-regulated learning and motivation, that were
traditionally viewed as a trait of the learner are now conceptualized as a series of events (Bannert et al. 2014;
Greene & Azevedo, 2009; Schmitz, 2006). Driving this conceptual change are indications that self-report
data have little relation with the actual student behaviour during learning (Veenman, 2011). These findings
point towards the need for new conceptualisations of these constructs. A temporal conceptualisation viewing
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self-regulated learning as a series of events that act differently over time and changing contexts, might
overcome these issues (Azevedo et al., 2010; Hadwin & Järvelä, 2011). For example, Malmberg and
colleagues (2014) show that students use different strategies and learning patterns when working on an ill-
structured task compared to a well-structured task. A series of events can be perceived as a process that
unfolds over time in a certain order (Reimann, 2009). For example, self-regulated learning processes of
successful students show a cyclical order among different strategies that repeat over time (Bannert et al.
2014). Moreover, Molenaar and Chiu (2014) found strong positive predictive relations between different
learning activities during collaborative learning over time. The changed conceptualization of constructs
raises new questions with regard to the characteristics of these constructs and their dynamic interplay with
the learning context.
Finally, an emerging interest is in connecting different levels of analysis (Hollenstein, 2013; Suthers,
Teplovs, de Laat, Oshima & Zeini 2011). This investigates of how macro-level phenomena can emerge from
and/or be constrained by different micro-level dynamics. For example, Chiu (2008) found that micro-
creativity in a group’ mathematical solutions can be sparked by a discourse pattern, namely a wrong idea
followed by disagreement among the group members. Temporal analysis can help develop an understanding
of how patterns unfold, providing insights into how learning is taking place (Chiu, 2008; Wise & Chiu,
2013).
Take together, the argument for temporal analysis is driven by the realisation that without careful
attention for temporal characteristics of constructs in learning and instruction research, we are reducing the
significance of our research and are unable to explain important aspects of learning and instruction.
3. A paradigm shift
As touched upon in the introduction, it is important to understand that advanced temporal analysis
entails a deviation from the traditional research paradigm used in learning and instruction (Reimann, 2009;
Schmitz, 2006). Often the variable-based approach is applied, which focuses on the analysis of variance
between independent and dependent variable(s). In contrast, the event-based approach looks at events
analysing the (dynamic) relations between the events (Reimann, 2009). This approach focuses on
researching the nature of these relations and their development over time. This reveals the temporal
characteristics of a construct and/or how different constructs interplay over time. For example, it can indicate
how a discussion among learners unfolds over time. Consistency and change in the behaviour of constructs
can be investigated by specifying these temporal characteristics (Schmitz, 2006).
Yet often reviewers in learning and instruction immediately ask the next question: can we explain
learning performance from temporal characteristics of the constructs? This question embodies the ”Holy
Grail” of temporal analysis and often constitutes a connection between our traditional variable-based
approach and the event-based approach. However, few (perhaps none) researchers have so far reached the
“Holy Grail”. Moreover, many of those initially aiming for this connection started to grow a realization that
there are valuable questions to be answered within analysing temporal characteristics themselves. An
example of such a research question is: Which sequences of learner actions (discuss, elaborate, summarize)
occur during collaborative learning? An example of a research question combining temporal characteristics
with learning performance is: Which sequences of learners’ actions during collaborative learning influence
learning performance positively? Overall temporal analysis in learning and instruction is innate to our
intuitive understanding of learning, but the operationalization of this understanding entails a deviation of our
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traditional research paradigm. Consequently, the nature of the questions addressed with temporal analysis
varies from our characteristic research questions in learning and instruction.
After all, Time is a highly complex construct that has been debated on from physics to philosophy.
Also within educational research, conceptualizations of different time scales (Lemke, 2000) and the use of
time in classrooms (Bloome et al., 2009; Mercer, 2008) have been discussed. Still, there is no framework that
conceptualizes dimensions of time and positions different temporal characteristics within these dimensions.
Research questions, therefore, focus on different dimensions of time and address conceptually different
temporal characteristics of constructs. In the next section, different dimensions of time important for learning
and instruction research are highlighted.
4. Different dimensions of time
So far, in our field when addressing temporal characteristics, we have encountered mainly frequency
analysis indicating the number of occurrences of a variable during a particular time window. This provides
insights into the prevalence of a construct during learning. For example, students receiving scaffolds during
learning apply more metacognitive activities compared to students that do not receive scaffolds (Molenaar et
al., 2011). Although informative, frequency analyses provide limited insights into the individual time-related
characteristics of the constructs researched. Even though this analysis showed that students perform more
metacognitive activities, we do not know the importance of their position in the learning process, their
duration or the rate at which these metacognitive activities occur during learning. Thus frequency analyses
treat the learning process as one holistic unit, ignoring the individual time-related characteristics of
constructs. Using the individual time-related characteristics allows for the analysis to illustrate how events
occur within the flow of continuous events in a particular time window. Examples are analyzing the
significance of the position of events, the duration of particular events and the rate of particular events within
the learning process (Molenaar & Wise, in prep). For example, planning at the start of a learning task was
found to be more productive for learning compared to planning latter on (Moos et al. 2008). Also students
monitor at a higher intensity and longer in more difficult tasks compared to easier tasks (Iiskala et al. 2010).
The dimension of time described above conceptualizes how constructs behave in a continuous flow of events
by examining the individual time-related characteristics of these events within the flow.
Another dimension of time in contrast to analysing events in a continuous flow, is analysing relative
arrangements of multiple events in time. Here the focus does not lie on the individual time-related
characteristics of events in a time window, but on how events are organized among each other. Examples are
both reoccurring processes and non-reoccurring processes (Molenaar & Wise, in prep). An example of a
reoccurring process is the cyclical notion of self-regulated learning, which suggests that orientation,
planning, monitoring and evaluation follow each other (Hadwin & Jarvela, 2013; Zimmerman, 2002). Non-
reoccurring transitions occur only once, for example, students who learn how to read progress from spelling
letters into the automatic detection of words (Verhoeven, 2004). Apart from reoccurring and non-reoccurring
patterns which both indicate a form of regular change, irregular change is another form of an arrangement of
multiple events that can be investigated. The notion of productive failure where collaborating students seem
to engage in chaotic interaction in the beginning of their collaboration is an example of irregular change
(Kapur, 2009). This seemingly unstructured process is of essential importance for their later learning. The
dimension of time described above conceptualizes how constructs behave in in relative arrangements of
multiple events by examining the organisation among these events.
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Without claiming that the above is a complete overview of temporal characteristics useful for the
field of learning and instruction, a clear distinction can be made between two dimensions of time, i.e.
focusing on individual events within the continuous flow of events or on relative arrangements of multiple
events (Molenaar & Wise, in prep). In order to push the conceptual understanding of time in our field, a
conceptual framework of looking at time and positioning temporal characteristics therein is important for
learning and instruction research to articulate and classify time-related research questions. Such a framework
can support conceptual clarity among researchers engaging in temporal analysis and organize and deepen
debates. Furthermore, it can be used as a roadmap to articulate temporal research questions, unravelling
temporal characteristics of different constructs.
5. An illustrative example of temporal analysis
In order to illustrate the above, I provide an example of a temporal analyses used to research socially
regulated learning. During collaborative learning, students support one another’s learning as they discuss,
elaborate, argue, confirm and regulate one another’s activities. We know that regulative activities such as
metacognitive (i.e., planning, monitoring) and relational activities (i.e., confirming, engaging) contribute
significantly to students’ learning (Molenaar et al., 2011). Yet, we know very little about how the group’s
socially regulative activities influence students’ cognition at a micro level during collaborative learning.
Therefore, we explored how sequences of students’ cognitive, metacognitive and relational activities affect
the likelihood of subsequent cognitive activities during collaborative learning and whether these
relationships differ across time (Molenaar & Chiu, 2014).
The data are from 18 triads (54 students) engaged in 51.338 conversation turns over 6 hours of
learning activities. The triads collaborated face-to-face while working in a computer based learning
environment. The primary school students were in grades 4, 5, and 6, and aged between 10 and 12. Statistical
discourse analysis, content and discourse analysis were used to analyse the learning activities. During
content analysis, each turn in the conversation was coded as cognitive (higher or lower cognition),
metacognitive (orientation, planning, monitoring and evaluation) or relational (confirm, deny, engage),
procedural or off task activities. Then, statistical discourse analysis (SDA) was used to examine the
sequential relations predicting lower and higher cognition (Chiu & Koo, 2005).
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Figure 1. Path diagram of standardized final multivariate outcome, multilevel cross-classification of low cognition
component. Solid lines indicate positive effects. Dashed lines indicate negative effects. Thicker lines indicate larger
effect sizes. *p < .05, **p < .01, ***p < .001. (Molenaar & Chiu, 2014; reproduced with permission)
We found that high cognitive, low cognitive, metacognitive and relational activities in recent
conversation turns were linked to the likelihood of low cognition in a conversation turn (see Figure 1).
Metacognitive activities in the form of planning (in the previous conversation turn or -1), monitoring (-1),
evaluating (2 conversation turns ago or -2), monitoring (-2), summarizing (-3) and monitoring (-3) all
increased the likelihood of low cognition, while orientation (-2) reduced it. Higher cognitive activities in
either of the last two conversation turns or low cognition in any of the last six conversation turns also
increased the likelihood of low cognition. Lastly, relational activities in the form of confirming and
engaging in any of the last two conversation turns increased the likelihood of low cognition.
This example analyzes temporal characteristics of arrangement of multiple events to understand how
these events act within the learning process. This type of analysis illustrates how different learning activities
alternate and fluctuate among collaborating students and emerge into socially regulated learning. The
findings show recurrent sequential relationships between cognitive, metacognitive and social relational
activities. Moreover, this analysis indicates that these patterns are rather stable over time.
Even though these analyses reveal important information about micro-level temporal interaction
among learning activities, an often received question is: “what do these relations among learning activities
mean for learning, i.e. which sequences should we encourage with instructional designs?” This question
embodies the “ holy grail” and has not been addressed yet. Although it is an important question, this inquiry
clearly indicates the need for a paradigm shift within our field. We need to learn to value results of temporal
analysis in their own right, providing important information about constructs in learning and instruction and
taking steps to defining micro level temporal theories of how constructs behave over time.
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6. Challenges
Apart from creating the awareness of the need for temporal research questions, there are a number of
other challenges that need to be addressed to forward temporal analysis in the field of learning and
instruction. As discussed in section 4, time can be conceptualised differently in our research (Bloome et al.,
2009; Lemke, 2000; Mercer, 2008). A conceptual framework to articulate different dimensions of time to
frame temporal characteristics and related research questions could enhance conceptual clarity and provide
ground for in-depth debate about time-related characteristics of individual events in the continues flow of
events or about the arrangements of multiple events over time.
Second, although there are many emerging methods such as visualizations (Reimann, 2009),
sequential lag analysis (Bakeman and Gottman, 1997), statistical discourse analysis (Chiu & Khoo, 2005),
temporal pattern analysis (Magnusson, 2000), Markov Modeling (Biswas, Kinnebrew & Segedy, 2012), data
mining (Robero et al. 2010), and dynamic systems (Hollenstein, 2013) used to explore time and order in
learning processes, we are only starting to explore the commonalities and differences among these methods.
Understanding about these techniques, as well as which learning and instruction questions can be answered
by their application, is required. Comparing different methods can enhance our understanding of temporal
characteristics of constructs in learning and instruction (e.g., via triangulation) and methodological issues
(e.g., which method is most appropriate for specific research questions?). Collaboration among researchers is
needed to create guidelines and to work towards a methodological framework for temporal analysis.
Third, when performing time-related research, we always “cut in time” i.e., we make an artificial
division in time units. This segmentation of time can be approached differently, that is at the level of
instructional units, time units or units of time in which a construct is acting homogeneously. For example,
determining the time window based on the frequency of occurrence of low cognition in the group discourse
(Molenaar & Chiu, 2014). Choices made about segmentation have important implications for the results,
and therefore, clear guidelines towards determining time windows should be formulated.
Fourth, granularity of our time related-research is an issue. The level at which we collect and code is
often at a micro level capturing very small units, such as events from electronic learning environments or
utterances in a dialogue. Our theories are usually defined at a macro level, explaining how different
constructs act. These different levels of granularity between coding and theory are a challenge for meaning
making. Aggregation of micro level variables to more macro level constructs can be a solution to this issue.
Yet, as with segmentation, decisions about granularity used in analysis also impacts results profoundly and
should therefore follow clear procedures to ensure quality standards. Moreover, combinations of different
research traditions, such as ethnographical approaches and data-mining methods, can help make connections
between macro level theory and micro level coding. A number of researchers have already indicated the need
for micro level temporal theories of constructs to support temporal analysis (Azevedo, 2014; Bannert et al.
2014; Molenaar & Chiu, 2014; Molenaar & Järvelä, 2014; Molenaar et al., 2011; Kuvulja et al. 2014; Winne,
2014).
Finally, until now, mainly exploratory studies have been done and there is a request from our
community to move toward to the Holy Grail, that is to establish that particular temporal characteristics
contribute to learning performance in particular ways. On the one hand, the Holy Grail will help confirm the
value of temporal analysis for the field of learning and instruction. Yet, as indicated above, linking these
analysis to learning performance is challenging. Collaboration among researchers is needed to overcome
these issues and create guidelines to work towards a uniform approach for event-based methods to enhance
our understanding of the temporal characteristics of learning and instruction.
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7. Conclusion
In the field of learning and instruction, there is an intuitive belief that temporality is important to
comprehend learning. In order for us, as a field, to make progress in understanding the temporal aspects of
learning, a number of challenges need to be overcome. The field needs to be aware that temporal analysis
often departs from the traditional research approach. In order to enhance this advancement, the field must
embrace a different kind of research question specifically related to temporal aspects of learning and
instruction.
Keypoints
Time deserves more attention in learning and instruction research
Temporal analysis entails a paradigm shift addressing a different type of research question
A conceptual framework of time can support framing temporal characteristics and research
questions
We need to advance our understanding of methodologies, time segmentation and meaning making
of temporal analysis
Acknowledgments
The thinking in this paper reflects idea’s developed and discussed during the various workshops “It’s about
time”. All participants in these workshops have contributed to the construction of this understanding and
especially my conversations with Alyssa Wise and Ming Ming Chiu.
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