Advances in temporal analysis in learning and
instruction
Inge Molenaara
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
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 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 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.
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).
Figure 1. (see pdf file) 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.
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.
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
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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