Once Again Another Author Confises Learning With Recalling

Introduction

As course sizes in pedagogy are increasing and applied science is impacting on education at all levels, these trends create pregnant challenges for teachers as they endeavour to support private students. Engineering science undoubtedly provides substantial advantages for students, enabling them to access data from around the planet easily and at whatever time. The advantages and disadvantages of the increased employ of technology have come up to low-cal over time as students increasingly engage with new innovations. In this review, we will address an issue that has go progressively evident in digital learning environments but is relevant to all educational settings, specially as class sizes abound. We will explore the difficulties in attempting to understand and account for the struggles students feel while learning a particular emphasis on what happens when students experience difficulties and become confused.

Running into bug while learning is often accompanied past an emotional response. Emotion, more than broadly, plays a vital role in the integration of new noesis with prior cognition. This has been plant to exist the example in brain imaging studies (e.one thousand., LeDoux, 1992), laboratory-based studies (e.g., Isen et al., 1987), and applied educational studies (eastward.g., Pekrun, 2005). A clear example of how emotion can impact on the learning process is where it creates an obstacle to learning, reflected in, for instance, the vast body of work that has examined the detrimental effect of anxiety on the learning of mathematics (Hembree, 1990). Similarly, confusion has been associated with blockages or impasses in the learning procedure (Kennedy and Society, 2016).

Despite its importance, understanding, identifying and responding to difficulties and the resulting emotions in learning tin be problematic, specially in larger classes and in digital environments. Without the affordances of synchronous face-to-confront human interaction in digital environments, emotions like confusion are difficult to detect. It is therefore challenging to respond to students with back up or feedback to aid their progress when they are stuck and go confused. Humans are uniquely tuned to respond to the emotional reactions of other humans (Damasio, 1994). Intuitively we know what it is like to feel confused as a result of a difficulty in the learning procedure, yet defoliation is not regarded as one of the "basic" emotions: like, for instance, happiness, sadness, and acrimony (Ekman, 2008). And while student defoliation is relatively easy for an experienced teacher to notice in face-to-face settings (Lepper and Woolverton, 2002), it is a circuitous emotion that is difficult to explicate scientifically (Silvia, 2010; Pekrun and Stephens, 2011). But we know that confusion is both commonly felt by students, is able to be diagnosed past teachers, and able to exist resolved productively with instructor back up (see for example, Lehman et al., 2008). Thus, at the about fundamental level, confusion is both widely experienced and relatively hands detected past teachers, despite the dubiousness about the exact relationship betwixt difficulties and emotional responses in learning. Thus, student emotions, such as defoliation, are relatively straightforward for experienced teachers to detect, sympathise and answer to in face-to-face settings with relatively small course sizes (encounter Woolfolk and Brooks, 1983; Woolf et al., 2009; Mainhard et al., 2018). The same is not true in digital environments or large classes. Emotions are less obvious to teachers when in that location are many students or when they interact with students via electronic methods (Wosnitza and Volet, 2005). This means that alternate practices are needed to answer to students when they experience difficulties in these emerging environments.

The increased difficulty in detecting and responding to student emotions is one of several key reasons why a deeper understanding of difficulties and associated emotional responses is needed as new technologies and increasing class sizes impact pedagogy. Digital learning environments, specially online or altitude learning environments, are oft explicitly designed and so that students will accept flexibility and autonomy in their studies. Students, when studying online or at a distance, are ofttimes able to access course material and resources in their own time (and place) and are often not constrained past centralized timetables. As a outcome, there is often a greater onus on students in these environments to be more democratic and self-directed in their learning (Huang, 2002). Thus, increased learning flexibility oftentimes leads to students having fewer opportunities for engaging with teaching staff and receiving feedback in real time (Mansour and Mupinga, 2007). While activities tin be made bachelor in the form of webinars and other synchronous formats, there remains a substantial responsibility on students to exist autonomous and brand good decisions nearly their own progress without requiring the existent-fourth dimension intervention of teaching staff.

Digital learning environments that largely provide cocky-directed students with autonomy and flexibility can potentially be created to discover and answer to student difficulties, only this potential has not however been realized (Arguel et al., 2017). A cardinal challenge for educational technology researchers and educators is to create digital environments that are ameliorate able to provide support for and potentially respond to difficulties and the resulting emotions such equally confusion, without the requirement of having a instructor on-call to back up students. For this to occur, sophisticated digital learning environments demand to exist created that tin can support students in their autonomous, personalized and cocky-directed learning and provide feedback that in some way, emulates what a teacher does in more traditional, face-to-confront settings.

In club for a digital learning environment to exist responsive to difficulties—or indeed to other emotions that bear on on learning—information technology is necessary for the organization to detect the emotions that students experience during their learning (Arguel et al., 2017). These emotional responses are the key indicator teachers use in face-to-face settings to decide when students are having problems. Given the difficulty of identifying emotions in digital learning environments in ways that humans can in contiguous environments, this is a specially vexing issue and one that has led to the growth of the burgeoning field of affective computing (Picard, 2000). A second requirement is that digital learning environments need to exist reactive to emotional responses such every bit confusion once these responses have been detected. For example, information technology would be useful if confused learners were given system-generated, programmed support to help them resolve their difficulties within the environment itself. Without a teacher present and without any automated support, it is possible that a educatee may succumb to their confusion, get frustrated and, as a upshot, undo entirely (D'Mello and Graesser, 2014). While information technology is difficult enough to decide when students become dislocated in these environments, it is even more complex to know when and how to intervene to prevent the defoliation from becoming boredom or frustration. Finally, it would be a distinct advantage if any response or feedback that a digital learning environment provided a confused student could be tailored and personalized to the private student and their learning pathway, progress and process (Social club, 2018). Teachers are able to quickly adapt to an individual student'south emotional responses in a classroom in smaller classes. This enables teachers to intervene with individualized, customized assistance and feedback for students, which can help them manage both their emotions and their approach to the particular learning activeness they are finding confusing. Constructive intervention represents a pregnant claiming for designers of digital learning environments as teachers are good at responding to educatee emotions in nuanced and personalized ways that are not easily programmed into a digital system.

Taken together, it is apparent that the increased use of digital learning environments has created a need for better understanding and intervening when students feel difficulties and become confused. This situation is, however, not helped by ongoing conjecture in the literature as to whether difficulties in the learning procedure resulting in confusion are detrimental or beneficial for learning (Arguel et al., 2017). For instance, Dweck (1986) argues that confusion is consistently detrimental to learning and is mediated by prior achievement, IQ scores, and confidence. She suggests that students who have poor prior accomplishment and confidence are at risk of attributing the experience of reaching a learning impasse and their resulting emotional response to their lack of bent. That is, students who become dislocated while completing a learning activity may interpret their confusion as a sign that they are incapable of learning the material. This statement aligns with a body of literature showing that persistent confusion can lead to frustration and colorlessness, which every bit a issue has a negative impact on learning (D'Mello and Graesser, 2014). More recently, still, research has suggested that difficulties resulting in confusion can do good student learning. This is maybe best exemplified in the inquiry on what have been labeled "desirable difficulties" (Bjork and Bjork, 2011), specific features of the learning situation that introduce beneficial difficulties that reliably raise learning. Along similar lines, D'Mello et al. (2014) found that inducing difficulties and confusion in an intelligent tutoring organization appeared to enhance learning. Moreover, some research has indicated that difficulties may be peculiarly beneficial for conceptual learning, where students sometimes need to overcome misconceptions before developing a more sophisticated agreement of the topic area (Kennedy and Lodge, 2016). For example, Chen et al. (2013) developed a predict-discover-explicate activity most commonly misconceived notions in electronics. Conflicting data was presented to students in the class of scenarios and the resulting confusion, when resolved, appeared to heighten student learning, specially in relation to correcting the misconceptions. What is apparent from this research is that at that place seems to be a complex mix of factors that lead to students experiencing difficulties and uncertainty about what kinds of outcomes occur as a result. The factors vary between students and the kinds of difficulties faced volition differ beyond noesis domains and task types.

From these few studies information technology is evident that experiencing difficulties and confusion might be beneficial for different students under different circumstances and that the role of confusion in productive learning is important to understand across different learning environments, knowledge domains, and types of learning activities. Dweck's (1986) piece of work indicates that defoliation may exist interpreted, managed and adapted to in different means by students depending on their levels of confidence and by achievements. On the other paw, the work of D'Mello et al. (2014) and Chen et al. (2013) suggests that confusion tin can help students' learning, particularly when conceptual learning or conceptual alter is the aim of the activity.

In this integrative review, we examine the literature on difficulties in learning. Nosotros focus hither on the means in which it might exist possible to detect confusion experienced as a result of difficulties and intervene when students are counterproductively dislocated. Our aim is to explore the means in which the difficulties students experience in learning could be harnessed for the purpose of enhancing their educational activity. If digital learning environments are to reach their potential, they must exist designed in a way to enable sophisticated back up and feedback to confused students, in ways that are similar to those a teacher tin can provide in small group confront-to-face settings.

Difficulties, Confusion, and Their Part in Learning

While defoliation is mutual in educational do and learning enquiry, generally speaking, it has been poorly defined and understood in the educational literature (Silvia, 2010). Confusion is frequently associated with reaching a cognitive impasse or "being stuck" while trying to larn something new (Woolf et al., 2009), and it is also ordinarily regarded as a negative emotional feel or something to exist avoided while learning ("Miss, assistance me, I am confused!"; see also Kort et al., 2001). Both of these aspects of confusion—being stuck and a feeling to be avoided—accept perhaps led to the everyday notion that confusion is detrimental to learning. While there is certainly research that suggests when defoliation persists to the bespeak of frustration, information technology commonly leads to negative outcomes and has a detrimental touch on understanding (Dweck, 1986; D'Mello and Graesser, 2011), every bit mentioned above, there are times when it may be beneficial to experience a cognitive impasse and the feeling of defoliation when learning.

When it comes to defining what confusion actually is, there has been some ambiguity as to the extent to which it is a cognitive or emotional phenomenon (D'Mello and Graesser, 2014). This doubtfulness stems from debates about whether or not emotions such as confusion crave some element of estimation in order for the subjective feel of the emotion to take form. These views are derived from an attributional perspective on emotion (Schachter and Vocalizer, 1962). The process, according to this perspective, is that confusion is the result of an individual'south attribution of an affective response to a preceding subjective experience. In other words, the educatee reaches an impasse that causes them some difficulty. As a outcome of the impasse, the educatee has some sort of emotional response to the situation they find themselves in. That emotional response is and then interpreted by the individual—they attribute meaning to it—which may be confusion (or anxiety, or excitement). In this way, the individual experiences or "attributes" the emotion of defoliation to the impasse. This estimation is especially important given that confusion in learning needs to be about some educational fabric attempting to exist understood past a educatee (Silvia, 2010). Still, the attributional procedure likewise suggests that at that place are substantial differences between individuals in terms of the attributions they make. Two students tin experience the exact aforementioned educational conditions and interpret them in vastly different means, leading one to be confused while the other experiences no such response. The interaction between subjective feel and content knowledge has led to confusion beingness defined as an "epistemic emotion" (Pekrun and Stephens, 2011). In other words, confusion can exist defined as an melancholia response that occurs in relation to how people come to know or understand something. When defined equally an epistemic emotion, confusion is considered to have both cerebral and affective components.

While it is reasonably clear that confusion has both cerebral and affective components, what is less obvious is whether difficulties in learning that result in defoliation are productive or unproductive in learning. The literature in this expanse is somewhat equivocal. D'Mello et al. (2014) examined students when learning about scientific reasoning using an intelligent tutoring system. Past inducing defoliation through the presentation of contradictory information, they were able to decide whether the experience of beingness dislocated contributed negatively or positively to learning outcomes. Two virtual agents were used in the intelligent tutoring system to nowadays information well-nigh the topic. In the confusion status, the information from the two agents was contradictory and thus confusing for students. D'Mello and colleagues found that when students completed the "confused" (i.east., contradictory) condition compared to when they completed the control (i.e., not-contradictory) condition they showed enhanced operation, and as a outcome, argued that confusion tin can be beneficial for learning. What remains unclear though is whether it was the difficulty, the subjective feel of confusion or a mixture of both that was responsible for the observed differences between the groups.

Numerous attempts have been made to induce difficulties and defoliation during learning to decide under what weather condition it contributes productively to student learning outcomes (due east.g., Lee et al., 2011; Lehman et al., 2013; Andres et al., 2014; Lodge and Kennedy, 2015). For instance, Grawemeyer et al. (2015) examined students' defoliation (and other emotions) during an activity in a digital learning environment that focussed on fractions. They found that, when provided with the advisable support at the right time, in the form of feedback and instruction, the difficulties experienced by students led to enhanced learning. Similarly, Muller et al. (2007) considered how videos including the presentation and subsequent correction (refutation) of a misconceived notion could create educatee confusion compared to videos which used more traditional didactic presentation methods. Students who watched physics videos using the refutation method were exposed to the most disruptive aspects of the concepts at the first of the video followed by an explanation of the commonly misconceived aspects of the content. Despite their higher levels of reported confusion, students in the refutation condition showed greater knowledge gains compared to students who watched the more than traditional videos. Muller and his colleagues argued that these findings are related to the extra mental try expended in trying to empathize the material when information technology is confusing.

These findings, and peculiarly Muller et al.'s (2007) interpretation of their results, suggests that, when students experience difficulties and defoliation, it may in fact serve equally a trigger to help them overcome whatever conceptual obstacles they see during their learning. Along like lines, Ohlsson (2011) argues that impasses and difficulties experienced in the learning procedure could exist effective triggers for students to rethink their learning approaches. When students achieve a conceptual impasse, this may serve as a cue that their current strategy or arroyo to the learning material is not effective, leading them to consider alternate strategies (D'Mello and Graesser, 2012). This perspective is consistent with research that has considered students' strategies for dealing with challenging material. In a series of experimental studies, Change et al. (2007) found that, when difficulties are introduced while people learn and reason near new information, it triggers a shift in strategy, activating a more systematic or analytic arroyo to the textile. It may be, therefore, that difficulties encountered during the learning procedure that are accompanied by a subjective feeling of confusion can atomic number 82 students to alter their learning strategies which may resolve the impasse, resulting in learning benefits. What this research and the findings suggest, even so, is that students need to be able to identify the trigger as a cue to change strategy, which necessitates a capacity for monitoring and self-regulation.

Findings from other studies accept found that confusion-inducing difficulties are non a productive function of the learning process despite the empirical research supporting the notion that confusion is beneficial in students' learning. For instance, Andres et al. (2014) examined defoliation while students engaged with a trouble solving-based video game designed to help them learn about physics. In this study, confusion negatively impacted on students' ability to solve the problems and, compared to students who were less confused, confused students were less likely to master the learning material. A 2d study, Poehnl and Bogner (2013), presented alternative scientific conceptions to a large group of ninth class students. Despite the apparently higher levels of confusion in this group compared to a group who were not exposed to the confusion-inducing alternate conceptions, this group performed worse in terms of the overall number of conceptions learned. Every bit such, there is alien testify about what role difficulties and resulting defoliation play in learning under different weather condition. Given the possibility that defoliation may operate as a trigger for action. This again highlights the possible role of self-regulation in this process. Twelvemonth nine students in the Poehnl and Bogner report may not have the same capacity to cocky-regulate their learning equally university students in the other studies discussed hither.

Perchance surprisingly, these are amidst the few empirical investigations to directly consider the impact of confusion on students' learning that have constitute it has a deleterious effect and those that have often involve younger students. However, research from other areas of learning and pedagogy, while not directly because the part of confusion in learning, take provided findings that are relevant to the role that difficulties and confusion may play in students' learning. The of import distinction seems to be the divergence between difficulties that students experience and the emotions that they feel as a result of these difficulties. While in that location has been limited research examining students' experiences of defoliation, at that place has been much piece of work done on trying to understand the function of difficulties in the learning process. For this review, we scanned the literature in educational psychology, experimental psychology, and education to wait for concepts that share a family unit resemblance (as per Wittgenstein, 1968) to the research on difficulties and confusion.

Research on Learning Challenges and Difficulties

Prominent among similar bodies of work that may assist in understanding how difficulties might contribute to learning in digital environments is research in areas such equally desirable difficulties (e.k., Bjork and Bjork, 2011), productive failure (e.g., Kapur, 2008), impasse-driven learning (e.g., VanLehn, 1988), cognitive disequilibrium (e.g., Graesser et al., 2005), and investigations of learning in discovery-based environments (east.thou., Moreno, 2004; Alfieri et al., 2011). Information technology is among these cognate fields of research that we may observe farther prove to back up the processes that pb to confusion being benign (or non) for learning. Our aim in attempting to compare and contrast this literature is to better empathize how difficulties and confusion may be beneficial to learning and under what conditions.

Studies of desirable difficulties typically consider how aspects of the learning process tin encumber learners, and how this process (or "difficulty") can lead to enhanced learning compared to learners not exposed to the difficulty (Bjork and Bjork, 2011). For example, Sungkhasettee et al. (2011) asked participants to study lists of words either upright or inverted. When learning the inverted words, participants demonstrated superior recall to atmospheric condition where the words were presented upright. In a similar study using more educationally relevant material, Adams et al. (2013) reported on a serial of studies where erroneous examples were given to students who were learning mathematics in a digital environment. Across these studies, Adams et al. found that the use of erroneous examples in mathematics teaching led to improvements in learning consistent with those observed in the broader literature on desirable difficulties. In order to depict the mechanism past which difficulties raise learning, Adams et al., argue that the utilise of incorrect examples encourages students to procedure the learning material in a different way, which leads to better retentivity and transfer of their understanding. They propose that students, by considering and engaging in alternative trouble solutions, procedure material more than securely and this is thought to be responsible for the enhanced learning observed (see besides McDaniel and Butler, 2011).

The growing trunk of research on desirable difficulties has raised some questions about what constitutes a beneficial difficulty in the learning process (Yue et al., 2013). For case, in a widely cited study, Diemand-Yauman et al. (2011) presented material to participants (study 1) and students (written report 2) in easy and hard to read fonts. They found that participants and students who studied material in hard to read fonts performed better when later quizzed on the fabric. The authors hypothesized that the difficulty in reading the disfluent font slowed the learning process down, leading to deeper encoding, thus creating a desirable difficulty. Subsequent attempts to replicate this disfluency-based desirable difficulty accept failed (e.g., Rummer et al., 2016), creating further uncertainty most what constitutes a desirable difficulty. Whatever the boundary atmospheric condition of desirable difficulties, it is apparent that certain kinds of difficulties in the learning process can reliably raise the encoding, storage and retrieval of information. Participants exposed to desirable difficulties in the bulk of the research on these effects to date have washed so predominantly under laboratory conditions. However, it is apparent that there were substantial advantages to introducing targeted difficulties in the learning process that are strong candidates for enhancing learning in live educational settings (Yan et al., 2017) and for further explaining how difficulties contribute to quality learning more broadly.

The principle of productive failure provides another possibility for framing the employ of difficulties to raise learning. Productive failure is a way of sequencing learning activities to requite students an opportunity to familiarize themselves with a circuitous problem or issue in a structured surround merely without significant pedagogy on the content of the material to exist learned (Kapur, 2015). Kapur (2014) tested groups of students who were given an opportunity to solve mathematics problems either before or after beingness given explicit instruction on the procedure associated with how to solve the problems. He found that the group of students who were given the opportunity to effort problems earlier being given explicit instructions, despite oftentimes declining in their first attempts, overall demonstrated significantly greater gains in learning compared to students who received instructions prior to attempting to solve problems. Without necessarily having the requisite skills or information to solve the problems they were presented with, students would oftentimes achieve an impasse in the learning process. Kapur (2015) argued that the impasse reached through the failed attempts at learning helps students generate more and different trouble-solving strategies through a process that enhances learning over both the shorter and the longer term. It should be noted here that the tasks used in productive failure studies are different to those used in studies of desirable difficulties. Studies on productive failure tend to apply more realistic problems given to students rather than tasks that rely more on memorisation.

Despite the different kinds of tasks used, in that location are clear parallels between the "failure" attribute of productive failure, and the "difficulties" encountered by students inside a desirable difficulty image (Kapur and Bielaczyc, 2012). In both situations, at that place is a deliberate strategy to encumber students' learning process and potentially trigger confusion. Unlike the work on desirable difficulties, nevertheless, much of the inquiry on productive failure has been carried out in naturalistic educational settings. This is achieved partly through the sequencing of the activeness. The lack of direct educational activity on the problem or issue frequently leads students to inevitably reach an impasse in the learning process that is seemingly accompanied by a sense of confusion (Hung et al., 2009). As summarized past Kapur (2015), the benefits of productive failure have been demonstrated many times in the peer-reviewed literature (eastward.thousand., Kapur, 2008; Kapur and Rummel, 2012). The results of these studies demonstrate that when students appoint in some problem solving first followed by just-in-time education when they reach an impasse (i.e., the process leads to failure), it leads to enhanced learning in educational situations that are designed to rely on direct instruction.

Productive failure shares some similarity with the notion of impasse-driven learning, which focuses on what happens when students reach a blockage in their learning. VanLehn (1988) suggests that when students reach an impasse in the learning process, it forces them to get into a problem-solving strategy he labeled "repair." In other words, students engage in a metacognitive process whereby they attempt to utilize trouble-solving strategies to overcome the impasse or seek help. In both cases, the necessity of engaging in "meta-level" thinking is hypothesized to atomic number 82 to more effective learning. This notion is similar to the statement fabricated by Ohlsson (2011) in relation to strategy shifting and over again highlights the importance of a capacity to monitor and self-regulate learning. In a test of impasse-driven learning, Blumberg et al. (2008) examined frequent and infrequent players of video games and asked them to draw their experiences as they worked through a novel video game. They establish that participants who engaged in video games regularly were more able to describe their trouble-solving strategies and moments of insight than those infrequently exposed to the types of impasses found in the games. To examine how this process applies to tutoring, VanLehn et al. (2003) analyzed dialogue in tutoring sessions on physics. Their results suggested that students were receptive to tutoring particularly when they reached an impasse in the learning process compared to when they were non at an impasse. The enquiry on impasse-driven learning over again suggests that there is something disquisitional virtually the metacognitive, learning or study strategies that students engage in when their learning process is disrupted or challenged in some way.

At the core of desirable difficulties, productive failure and impasse driven learning is the notion that a difficulty or deliberately designed challenges are important for learning (VanLehn, 1988; Ohlsson, 2011). Contemporary, and increasingly popular models of instruction, rooted in Bruner'south (1961) notion of discovery-based learning likewise share this characteristic. Discovery-based models of education and learning such every bit trouble-based learning typically present students with an ill-structured scenario, situation or problem, which they discuss, often in groups, and investigate in order to resolve. Students, in discussing the trouble among themselves with or without a more expert facilitator, inevitably encounter material that they do not understand, that is confusing, and represents an impasse in their investigation of the trouble. These impasses are central to the trouble-based learning instructional model every bit they both drive the learning process (becoming the "learning bug" that guide students' learning and guide their investigations of the problem) and they too are said to human action equally intrinsic motivators for students as they try to resolve the problem (Schmidt, 1993).

Given some of the core similarities between these theoretical models,—productive failure, impasse driven learning, desirable difficulties, and problem-based learning—a cardinal question for educational researchers is: what are the underlying cognitive and learning processes that both bring about student confusion, and underpin the potential learning benefits derived from it? As well, how practice these processes differ betwixt private students, learning unlike material, and engaged in dissimilar types of tasks? Graesser and D'Mello (2012) suggest that the prime candidate for this underpinning process is cognitive disequilibrium. The notion of cerebral disequilibrium is derived from Piaget'due south piece of work on cerebral development (Piaget, 1964). It occurs when there is an imbalance created when new information does not seamlessly integrate with existing mental schema. It is plausible then that confusion is the upshot of certain types of difficulties in the learning process, namely those that lead to an impasse underpinned past cerebral disequilibrium. In attempting to pattern for and provide interventions for productive challenges then, what appears to be important is not the introduction of difficulties per se simply the introduction of difficulties that lead to an impasse and a sense of disequilibrium. Based on the research across these domains this, in turn, is hypothesized to pb to a alter in learning approach or problem-solving strategy that can heighten learning.

A Framework for Understanding and Seeing Difficulties and Resulting Defoliation in Learning

From this review, it seems clear that difficulties experienced during learning and resulting in defoliation tin be either productive or unproductive depending on the arrangement of and human relationship between a range of variables within a learning environs. These include the type of learning activity, the noesis domain being learned, and private differences such every bit how students attribute difficulties and their capacity for self-regulated learning. For whatever particular learning or content area, the caste to which difficulties are experienced past a learner, and whether the feel of the resulting epistemic emotion volition be productive or unproductive, is a upshot of a complex relationship betwixt:

(i) Individually-based variables, such as prior cognition, cocky-efficacy, and cocky-regulation;

(ii) The sequence, structure and design of learning tasks and activities; and

(iii) The design and timeliness feedback, guidance, and support provided to students during the learning activeness or task.

A key claiming for educational researchers is to decide what sets of relationships between what variables lead to adaptive and maladaptive learning processes and outcomes in digital learning environments.

The review of the literature likewise suggests two learning processes could be promoted when students experience confusion: ane general and ane specific. The first, more general, procedure is that difficulties encourage students to invest more "mental effort" in their learning; they somehow work harder cognitively—through attention or concentration—to resolve the conceptual impasse and the confusion that has resulted from it. The second is that students, when piqued by a conceptual impasse and the resulting feelings of confusion, actively generate and prefer alternative approaches to the learning material they are seeking to understand. This 2nd process suggests that students do not simply invest a greater endeavour in their learning; information technology suggests that they investigate and adopt alternative report approaches and strategies, which they then apply. In guild for this second process to occur, students need to exist sufficiently able to monitor their progress and understand how to take action on the ground of their experience of difficulty or the reaching of an impasse.

Finally, this review suggests that insurmountable learning difficulties may arise when students experience too much confusion or when defoliation persists for too long. As discussed by D'Mello and Graesser (2014) one of the about important factors in the beneficial outcome of defoliation is that it is resolved. Unresolved, persistent confusion leads to frustration, boredom and therefore is detrimental for learning. In an example of this fragile balance in action, Lee et al. (2011) examined confusion while novices attempted to acquire how to write computer code. They found that overcoming confusion can enhance learning but, when it remains unresolved, information technology leads to deleterious furnishings on student achievement. This ascertainment speaks to the importance of addressing educatee confusion in a timely and personalized way. Nonetheless, given the complexities introduced by the individual differences between students, this is non a straightforward task.

In many ways, these features of defoliation are captured in Graesser's (2011) notion of a "zone of optimal defoliation" (ZOC). Reminiscent of Vygotsky's (1978) concept of the zone of proximal development, the ZOC suggests that it is important non to have also piddling or as well much difficulty merely to aim to have just the correct amount. If educators and educational designers aimed to create challenges and induce a change in learning strategy as a deliberate tactic to promote conceptual modify, students would demand to experience sufficient subjective difficulty for the impasse in the learning process to be experienced every bit confusion. Nevertheless, if too much or persistent confusion is experienced, it will lead to frustration, hopelessness, boredom and giving up. To utilize difficulties every bit a deliberate instructional strategy in digital learning environments is, therefore, a double-edged sword. If students are not sufficiently engaged to get confused and redress their way of approaching the activeness, they tin can and then become bored and potentially regress back to their initial conception. If students can be guided and supported through their defoliation, still, it can then pb to the productive learning outcomes reported in the empirical literature. That, in essence, is the ZOC.

One ongoing issue with the notion of "optimal confusion" is that it is difficult to determine what separates productive from not-productive confusion as learning unfolds. Given the complexities involved due to private responses to difficulties in learning, the threshold at which effective confusion becomes not-productive frustration or boredom will differ markedly between individuals (Kennedy and Guild, 2016). Identifying where a pupil might be along the confusion continuum in advance of knowing the outcome of the learning activity is a significant claiming. Kennedy and Gild institute that there were markers evident in trace information suggestive of students crossing the threshold into unproductive forms of defoliation. For instance, extended delays in progress observed as meaning time lags between interactions or rapid cycling through activities are possible indicators of boredom or frustration respectively. Inferring in real time whether students are experiencing confusion that is productive or unproductive remains a challenge but in that location is some emerging prove that information and analytics could be used to help predict how students are tracking and provide feedback and support independent of knowing the outcome (Arguel et al., 2019).

Based on Graesser's (2011) "ZOC" and, using cognitive disequilibrium as a framing machinery for the important role of confusion in learning, we propose a framework for confusion in digital learning environments (see Figure 1). From the pinnacle of Figure ane, a learning event can be specifically designed to create cerebral disequilibrium. An example of this is the approach used by Muller et al. (2008) to create disequilibrium in videos. In this written report, the researchers created disequilibrium past focussing on misconceptions equally a core instructional strategy, the disequilibrium beingness generated through the altitude betwixt what people think they know and the accustomed scientific understanding. From there, disequilibrium is generated as a cause of an impasse in the learning process. At this stage, students will move into the ZOC so long equally they are sufficiently engaged and aspect the impasse to be confusing. If this occurs in a productive way and the student has sufficient metacognitive awareness and skill to recognize the defoliation every bit a cue to change strategy, the disequilibrium will be effectively resolved, conceptual change volition occur, and students will motility on to another learning result. If the defoliation becomes persistent, on the other hand, then students may possibly move into the zone of sub-optimal confusion (ZOSOC). When this occurs, the defoliation becomes unproductive and leads to possible frustration and/or colorlessness. Again, information technology is difficult to determine in real time when and how this occurs and that remains a challenge for future research to examine. The model proposed here builds on similar previous work past D'Mello and Graesser (2014) merely is particularly focused on farther elucidating both the underpinning processes and the characteristics of the learning design that might influence both the initiation of confusion and its resolution.

Figure 1. Conceptual framework for the zones of optimal and sub-optimal confusion.

Implications of the Framework

If information technology can be assumed that confusion is beneficial for learning under some circumstances then it is worth considering the implications of this for learning design. The creation of disequilibrium and confusion is important to both engage students and create the uncertainty required to help them develop conceptual knowledge. A learning event that is aimed at creating this disequilibrium will need to be designed with the aim of both getting students into the ZOC and making sure that they do not enter the ZOSOC. Enticing students to enter the ZOC has been achieved in numerous means as described above. For example, the material presented or the medium through which it is presented tin be contradictory, counterintuitive or the surround can have picayune to no guidance equally in pure discovery-based learning and, to a lesser extent, productive failure. Taken together, there would appear to be many ways to lure students into the ZOC. That said, there are no guarantees that students volition enter this ZOC. If a student has loftier levels of prior knowledge or is highly confident, for example, they may persist at a task with renewed vigor rather than attribute an impasse equally confusing (Arguel et al., 2016).

When it does occur, ensuring the defoliation leads to a productive event is more challenging equally it requires the students themselves resolving the disequilibrium, a timely intervention from a instructor, or in a fashion that can exist automatically supported in a digital learning environment. Thus, there appear to exist 2 broad options for ensuring defoliation leads to productive outcomes. As alluded to above, the development of effective self-regulation in learning is i way of ensuring that students movement from existence confused to effectively learning. While students' skills in self-regulation are something they may at to the lowest degree partly bring to a learning issue, in that location is also potential for edifice in interventions to aid with self-regulation into the learning environment (Lodge et al., 2018). For example, if students did change their strategy or approach to a learning consequence, this creates an opportunity for them to reverberate on the change in their arroyo and consider how such a strategy might exist useful in time to come learning situations. And so, while in that location are opportunities for helping students to finer learn new fabric, at that place are besides possibilities in these situations for students to consider the strategies they use when learning more than broadly. In a very physical style, one intervention strategy is to help students to understand that difficulties and confusion as part of the learning procedure are perfectly normal and, indeed, necessary in many instances. Helping students to meet confusion as a cue to endeavor a dissimilar arroyo rather than see it is a sign that they are incapable would be a unproblematic way to meliorate students' chapters to deal with difficult and confusing elements of learning.

A 2d option for ensuring that students effectively pass through the ZOC and accomplish productive learning outcomes is to use feedback. Feedback can take many different forms in digital learning environments thus providing many options for intervening when students appear to be confused. The disquisitional aspect of any intervention on confusion to avert having students enter into the ZOSOC will exist to personalize that feedback past taking into account their prior knowledge (Lehman et al., 2012). Intelligent tutoring systems have some capacity for this level of personalisation. Still, much remains to be done before these systems tin be regarded as being truly adaptive to the affective components of student learning and applied at calibration (Baker, 2016). Equally a proof of concept though, there are examples of sophisticated adaptive systems that have been congenital to provide real fourth dimension feedback and prompts based on pupil performance as they progress through procedural tasks. For example, adaptive systems have long been available to provide information-driven feedback and prompts to trainee surgeons (Piromchai et al., 2017), and dentists (Perry et al., 2015). That it is possible to create systems that can employ information about student interaction to inform feedback interventions suggest that it is possible to build systems that will piece of work across different knowledge domains to respond to students having difficulties.

In the acting, while intelligent tutoring and other adaptive systems built on auto learning and artificial intelligence mature, there are possibilities for building digital learning environments to cater for difficulties and resulting confusion. Nearly prominent among these are the evolution of sophisticated learning designs that tin can answer to student defoliation through enhancing student self-regulation and providing feedback in the form of hints or determinative information nearly the strategies or approaches being used. That is not to say that the development of such systems will be easy. Part of the arroyo to helping students become improve equipped to deal with difficulties and defoliation needs to be to accost the notion that difficulties are inherently detrimental and an indicator that students are not capable.

Determination

Difficulties and the confusion that often results are difficult to notice, manage, and reply to in digital learning environments and large classes compared to smaller group contiguous settings. Despite this, in this paper nosotros take argued that difficulties and defoliation are important in the procedure of learning, specially when students are developing more sophisticated understandings of complex concepts. Work on desirable difficulties, impasse driven learning, productive failure, and pure discovery-based learning all provide clues equally to how defoliation could be benign for learning. The cosmos of a sense of cognitive disequilibrium appears to exist a vital element and the confusion needs to be effectively resolved by helping students laissez passer through the ZOC without them entering the ZOSOC. Nosotros have attempted here to provide a conceptual model for the process past which students pass through this optimal zone. Our promise is that this volition help to outline the process of the development and resolution of defoliation so that researchers and learning designers tin proceed to develop methods for ensuring students attain productive outcomes as a consequence of becoming dislocated.

Author Contributions

JL, GK, LL, AA, and MP contributed to the conceptualization, research, and writing of this commodity.

Funding

The authors of this review received funding from the Australian Enquiry Quango for the work in this review as part of a Special Research Initiative (Grant number: SRI20300015).

Disharmonize of Interest Statement

The authors declare that the research was conducted in the absence of whatsoever commercial or fiscal relationships that could be construed every bit a potential conflict of involvement.

Acknowledgments

The authors acknowledge the contributions of Dr. Paula de Barba toward this project.

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Source: https://www.frontiersin.org/articles/10.3389/feduc.2018.00049/full