Seftons 2013 Understanding Learning in Not School Environments Review

Introduction

As class sizes in instruction are increasing and engineering science is impacting on education at all levels, these trends create significant challenges for teachers equally they attempt to support individual students. Technology undoubtedly provides substantial advantages for students, enabling them to admission information from around the planet easily and at any time. The advantages and disadvantages of the increased use of engineering have come to light over time as students increasingly engage with new innovations. In this review, we volition address an issue that has get progressively evident in digital learning environments but is relevant to all educational settings, particularly equally class sizes grow. We will explore the difficulties in attempting to understand and business relationship for the struggles students experience while learning a particular accent on what happens when students experience difficulties and become confused.

Running into bug while learning is oftentimes accompanied by an emotional response. Emotion, more broadly, plays a vital function in the integration of new noesis with prior knowledge. This has been found to be the instance in brain imaging studies (due east.g., LeDoux, 1992), laboratory-based studies (e.g., Isen et al., 1987), and applied educational studies (e.g., Pekrun, 2005). A clear case of how emotion can affect on the learning process is where information technology creates an obstruction to learning, reflected in, for example, the vast body of piece of work that has examined the detrimental issue of anxiety on the learning of mathematics (Hembree, 1990). Similarly, defoliation has been associated with blockages or impasses in the learning procedure (Kennedy and Lodge, 2016).

Despite its importance, understanding, identifying and responding to difficulties and the resulting emotions in learning can be problematic, particularly in larger classes and in digital environments. Without the affordances of synchronous contiguous homo interaction in digital environments, emotions similar defoliation are difficult to detect. It is therefore challenging to answer to students with support or feedback to aid their progress when they are stuck and become confused. Humans are uniquely tuned to respond to the emotional reactions of other humans (Damasio, 1994). Intuitively we know what it is similar to feel confused as a result of a difficulty in the learning procedure, yet confusion is not regarded as 1 of the "basic" emotions: similar, for case, happiness, sadness, and acrimony (Ekman, 2008). And while student confusion is relatively like shooting fish in a barrel for an experienced teacher to discover in confront-to-face settings (Lepper and Woolverton, 2002), it is a complex emotion that is difficult to explain 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 support (encounter for example, Lehman et al., 2008). Thus, at the most fundamental level, confusion is both widely experienced and relatively hands detected by teachers, despite the uncertainty about the verbal relationship between difficulties and emotional responses in learning. Thus, educatee emotions, such as defoliation, are relatively straightforward for experienced teachers to detect, sympathize and respond to in face-to-face settings with relatively small class sizes (run across Woolfolk and Brooks, 1983; Woolf et al., 2009; Mainhard et al., 2018). The aforementioned is not truthful in digital environments or large classes. Emotions are less obvious to teachers when there are many students or when they collaborate with students via electronic methods (Wosnitza and Volet, 2005). This means that alternating practices are needed to answer to students when they experience difficulties in these emerging environments.

The increased difficulty in detecting and responding to pupil emotions is 1 of several primal reasons why a deeper understanding of difficulties and associated emotional responses is needed every bit new technologies and increasing course sizes bear on education. Digital learning environments, especially online or distance learning environments, are ofttimes explicitly designed so that students will have flexibility and autonomy in their studies. Students, when studying online or at a distance, are often able to admission course textile and resources in their own time (and place) and are frequently not constrained past centralized timetables. Equally a result, there is often a greater onus on students in these environments to be more autonomous and self-directed in their learning (Huang, 2002). Thus, increased learning flexibility frequently leads to students having fewer opportunities for engaging with education staff and receiving feedback in real fourth dimension (Mansour and Mupinga, 2007). While activities can be fabricated bachelor in the form of webinars and other synchronous formats, at that place remains a substantial responsibility on students to be autonomous and make good decisions about their ain progress without requiring the real-fourth dimension intervention of education staff.

Digital learning environments that largely provide self-directed students with autonomy and flexibility can potentially exist created to find and respond to student difficulties, just this potential has not still been realized (Arguel et al., 2017). A key claiming for educational applied science researchers and educators is to create digital environments that are better able to provide support for and potentially respond to difficulties and the resulting emotions such as confusion, without the requirement of having a instructor on-phone call to back up students. For this to occur, sophisticated digital learning environments need to be created that tin can support students in their autonomous, personalized and self-directed learning and provide feedback that in some manner, emulates what a teacher does in more than traditional, contiguous settings.

In lodge for a digital learning environment to exist responsive to difficulties—or indeed to other emotions that bear upon on learning—it is necessary for the system to observe 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 determine when students are having problems. Given the difficulty of identifying emotions in digital learning environments in ways that humans can in face-to-face environments, this is a particularly vexing consequence 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 be reactive to emotional responses such as defoliation once these responses take been detected. For example, it would be useful if dislocated learners were given organization-generated, programmed support to help them resolve their difficulties within the environment itself. Without a teacher nowadays and without any automated back up, it is possible that a pupil may succumb to their defoliation, get frustrated and, as a result, disengage entirely (D'Mello and Graesser, 2014). While it is difficult enough to determine when students become dislocated in these environments, information technology is even more complex to know when and how to intervene to foreclose the confusion from becoming boredom or frustration. Finally, it would be a distinct reward if whatsoever response or feedback that a digital learning environment provided a confused pupil could be tailored and personalized to the individual student and their learning pathway, progress and procedure (Society, 2018). Teachers are able to quickly adjust to an individual student's emotional responses in a classroom in smaller classes. This enables teachers to intervene with individualized, customized assistance and feedback for students, which tin can help them manage both their emotions and their approach to the particular learning action they are finding confusing. Constructive intervention represents a meaning claiming for designers of digital learning environments as teachers are adept at responding to pupil emotions in nuanced and personalized ways that are not easily programmed into a digital organization.

Taken together, it is apparent that the increased utilise of digital learning environments has created a need for meliorate agreement and intervening when students experience difficulties and become confused. This situation is, however, not helped past ongoing theorize in the literature as to whether difficulties in the learning process resulting in defoliation 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 past prior achievement, IQ scores, and confidence. She suggests that students who have poor prior achievement and confidence are at risk of attributing the feel of reaching a learning impasse and their resulting emotional response to their lack of aptitude. 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 textile. This argument aligns with a trunk of literature showing that persistent confusion can lead to frustration and boredom, which as a result has a negative impact on learning (D'Mello and Graesser, 2014). More recently, nevertheless, inquiry has suggested that difficulties resulting in confusion can benefit student learning. This is perhaps best exemplified in the enquiry on what have been labeled "desirable difficulties" (Bjork and Bjork, 2011), specific features of the learning situation that innovate beneficial difficulties that reliably heighten learning. Along similar lines, D'Mello et al. (2014) found that inducing difficulties and confusion in an intelligent tutoring system appeared to raise learning. Moreover, some enquiry has indicated that difficulties may be peculiarly beneficial for conceptual learning, where students sometimes demand to overcome misconceptions earlier developing a more sophisticated understanding of the topic surface area (Kennedy and Lodge, 2016). For instance, Chen et al. (2013) developed a predict-detect-explicate activity about commonly misconceived notions in electronics. Conflicting information was presented to students in the form of scenarios and the resulting defoliation, when resolved, appeared to enhance student learning, particularly in relation to correcting the misconceptions. What is apparent from this research is that there seems to be a complex mix of factors that pb to students experiencing difficulties and doubtfulness about what kinds of outcomes occur as a effect. The factors vary betwixt students and the kinds of difficulties faced will differ across knowledge domains and task types.

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

In this integrative review, we examine the literature on difficulties in learning. We focus hither on the means in which information technology might be possible to discover confusion experienced as a result of difficulties and intervene when students are counterproductively confused. Our aim is to explore the ways in which the difficulties students experience in learning could be harnessed for the purpose of enhancing their didactics. If digital learning environments are to reach their potential, they must be designed in a fashion to enable sophisticated support and feedback to confused students, in means that are similar to those a teacher can provide in pocket-sized group confront-to-face settings.

Difficulties, Confusion, and Their Office in Learning

While confusion is common in educational practice and learning research, 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 learn something new (Woolf et al., 2009), and it is also ordinarily regarded as a negative emotional experience or something to be avoided while learning ("Miss, assist me, I am confused!"; come across as well Kort et al., 2001). Both of these aspects of confusion—beingness stuck and a feeling to exist avoided—have perhaps led to the everyday notion that defoliation is detrimental to learning. While at that place is certainly research that suggests when defoliation persists to the point of frustration, it ordinarily leads to negative outcomes and has a detrimental impact on understanding (Dweck, 1986; D'Mello and Graesser, 2011), as mentioned higher up, at that place are times when information technology may be beneficial to experience a cognitive impasse and the feeling of confusion when learning.

When it comes to defining what confusion really is, there has been some ambiguity every bit to the extent to which it is a cognitive or emotional phenomenon (D'Mello and Graesser, 2014). This uncertainty stems from debates almost whether or not emotions such every bit defoliation require some chemical element of interpretation in guild for the subjective experience of the emotion to have form. These views are derived from an attributional perspective on emotion (Schachter and Vocaliser, 1962). The process, according to this perspective, is that confusion is the result of an private'southward attribution of an melancholia response to a preceding subjective experience. In other words, the student reaches an impasse that causes them some difficulty. As a issue of the impasse, the student has some sort of emotional response to the situation they find themselves in. That emotional response is and so interpreted past the individual—they attribute meaning to information technology—which may be confusion (or anxiety, or excitement). In this way, the individual experiences or "attributes" the emotion of defoliation to the impasse. This interpretation is especially important given that confusion in learning needs to be about some educational material attempting to be understood past a educatee (Silvia, 2010). However, the attributional process also suggests that there are substantial differences betwixt individuals in terms of the attributions they make. Two students tin feel the exact same educational conditions and interpret them in vastly different means, leading i to exist dislocated while the other experiences no such response. The interaction betwixt subjective feel and content knowledge has led to confusion existence divers as an "epistemic emotion" (Pekrun and Stephens, 2011). In other words, confusion can be defined as an affective response that occurs in relation to how people come to know or empathize something. When defined as an epistemic emotion, defoliation is considered to have both cognitive and affective components.

While it is reasonably clear that confusion has both cognitive 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 area is somewhat equivocal. D'Mello et al. (2014) examined students when learning virtually scientific reasoning using an intelligent tutoring organisation. By inducing defoliation through the presentation of contradictory information, they were able to decide whether the experience of existence confused contributed negatively or positively to learning outcomes. Two virtual agents were used in the intelligent tutoring organization to nowadays information about the topic. In the confusion status, the information from the two agents was contradictory and thus confusing for students. D'Mello and colleagues constitute that when students completed the "confused" (i.e., contradictory) condition compared to when they completed the control (i.e., not-contradictory) condition they showed enhanced operation, and every bit a result, argued that confusion tin can be beneficial for learning. What remains unclear though is whether it was the difficulty, the subjective experience 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 confusion during learning to determine nether what conditions it contributes productively to pupil learning outcomes (e.g., Lee et al., 2011; Lehman et al., 2013; Andres et al., 2014; Lodge and Kennedy, 2015). For example, Grawemeyer et al. (2015) examined students' confusion (and other emotions) during an activity in a digital learning environment that focussed on fractions. They constitute that, when provided with the appropriate 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 offset of the video followed by an caption of the commonly misconceived aspects of the content. Despite their higher levels of reported confusion, students in the refutation condition showed greater cognition gains compared to students who watched the more traditional videos. Muller and his colleagues argued that these findings are related to the extra mental effort expended in trying to understand the material when it is disruptive.

These findings, and especially Muller et al.'due south (2007) interpretation of their results, suggests that, when students experience difficulties and defoliation, it may in fact serve as a trigger to aid them overcome whatsoever conceptual obstacles they encounter during their learning. Along similar lines, Ohlsson (2011) argues that impasses and difficulties experienced in the learning procedure could be constructive triggers for students to rethink their learning approaches. When students reach a conceptual impasse, this may serve as a cue that their current strategy or approach 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 textile. In a series of experimental studies, Modify et al. (2007) found that, when difficulties are introduced while people learn and reason about new information, it triggers a shift in strategy, activating a more systematic or analytic approach to the material. Information technology may be, therefore, that difficulties encountered during the learning process that are accompanied by a subjective feeling of defoliation tin can lead students to alter their learning strategies which may resolve the impasse, resulting in learning benefits. What this research and the findings propose, however, is that students need to be able to identify the trigger equally a cue to change strategy, which necessitates a capacity for monitoring and cocky-regulation.

Findings from other studies have found that defoliation-inducing difficulties are not a productive office of the learning process despite the empirical enquiry supporting the notion that defoliation is beneficial in students' learning. For case, Andres et al. (2014) examined defoliation while students engaged with a problem solving-based video game designed to help them acquire about physics. In this study, confusion negatively impacted on students' power to solve the issues and, compared to students who were less dislocated, dislocated students were less likely to master the learning material. A second study, Poehnl and Bogner (2013), presented alternative scientific conceptions to a large grouping of ninth form students. Despite the plainly college levels of confusion in this group compared to a group who were non exposed to the confusion-inducing alternate conceptions, this grouping performed worse in terms of the overall number of conceptions learned. As such, there is conflicting evidence about what role difficulties and resulting defoliation play in learning under different conditions. Given the possibility that confusion may operate as a trigger for action. This once again highlights the possible role of cocky-regulation in this process. Year nine students in the Poehnl and Bogner written report may not take the same capacity to self-regulate their learning as university students in the other studies discussed here.

Perhaps surprisingly, these are among the few empirical investigations to straight consider the impact of confusion on students' learning that take found it has a deleterious outcome and those that have often involve younger students. Notwithstanding, research from other areas of learning and educational activity, while not direct considering the role of confusion in learning, have provided findings that are relevant to the role that difficulties and confusion may play in students' learning. The important distinction seems to be the divergence between difficulties that students experience and the emotions that they experience every bit a consequence of these difficulties. While there has been express research examining students' experiences of confusion, in that location has been much work done on trying to understand the role of difficulties in the learning process. For this review, nosotros scanned the literature in educational psychology, experimental psychology, and education to look for concepts that share a family unit resemblance (equally per Wittgenstein, 1968) to the research on difficulties and confusion.

Research on Learning Challenges and Difficulties

Prominent among like bodies of work that may assist in understanding how difficulties might contribute to learning in digital environments is research in areas such as desirable difficulties (e.k., Bjork and Bjork, 2011), productive failure (due east.m., Kapur, 2008), impasse-driven learning (east.one thousand., VanLehn, 1988), cognitive disequilibrium (east.k., Graesser et al., 2005), and investigations of learning in discovery-based environments (e.g., Moreno, 2004; Alfieri et al., 2011). It is among these cognate fields of enquiry that we may detect further evidence to support the processes that lead to defoliation being beneficial (or not) for learning. Our aim in attempting to compare and contrast this literature is to meliorate understand how difficulties and confusion may be benign to learning and nether what weather condition.

Studies of desirable difficulties typically consider how aspects of the learning procedure can encumber learners, and how this process (or "difficulty") tin can atomic number 82 to enhanced learning compared to learners non 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 weather condition where the words were presented upright. In a similar study using more educationally relevant material, Adams et al. (2013) reported on a series 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 instruction led to improvements in learning consequent with those observed in the broader literature on desirable difficulties. In order to draw the mechanism by which difficulties enhance learning, Adams et al., argue that the use of incorrect examples encourages students to process the learning material in a different way, which leads to meliorate retention and transfer of their understanding. They propose that students, by considering and engaging in alternative trouble solutions, process material more deeply and this is idea to exist responsible for the enhanced learning observed (see likewise McDaniel and Butler, 2011).

The growing body of research on desirable difficulties has raised some questions well-nigh what constitutes a beneficial difficulty in the learning process (Yue et al., 2013). For example, in a widely cited study, Diemand-Yauman et al. (2011) presented textile to participants (study i) and students (study 2) in easy and difficult to read fonts. They found that participants and students who studied material in hard to read fonts performed improve when later quizzed on the material. 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 have failed (east.g., Rummer et al., 2016), creating further incertitude nearly what constitutes a desirable difficulty. Whatsoever the boundary conditions of desirable difficulties, information technology 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 majority of the research on these effects to date have done so predominantly under laboratory atmospheric condition. However, it is apparent that there were substantial advantages to introducing targeted difficulties in the learning process that are potent candidates for enhancing learning in alive educational settings (Yan et al., 2017) and for farther explaining how difficulties contribute to quality learning more broadly.

The principle of productive failure provides another possibility for framing the use of difficulties to enhance 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 environment but without significant pedagogy on the content of the material to be learned (Kapur, 2015). Kapur (2014) tested groups of students who were given an opportunity to solve mathematics problems either before or after being 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 attempt problems before being given explicit instructions, despite oft failing 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 often achieve an impasse in the learning procedure. Kapur (2015) argued that the impasse reached through the failed attempts at learning helps students generate more than and different problem-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 use more realistic problems given to students rather than tasks that rely more than on memorisation.

Despite the different kinds of tasks used, at that place are articulate parallels betwixt the "failure" aspect of productive failure, and the "difficulties" encountered past students within a desirable difficulty paradigm (Kapur and Bielaczyc, 2012). In both situations, there is a deliberate strategy to encumber students' learning process and potentially trigger defoliation. Unlike the work on desirable difficulties, even so, much of the research on productive failure has been carried out in naturalistic educational settings. This is accomplished partly through the sequencing of the activity. The lack of direct instruction on the problem or event oftentimes 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 by Kapur (2015), the benefits of productive failure have been demonstrated many times in the peer-reviewed literature (eastward.g., Kapur, 2008; Kapur and Rummel, 2012). The results of these studies demonstrate that when students engage in some problem solving first followed by just-in-time instruction when they accomplish an impasse (i.east., 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 go into a trouble-solving strategy he labeled "repair." In other words, students appoint in a metacognitive process whereby they attempt to use problem-solving strategies to overcome the impasse or seek aid. In both cases, the necessity of engaging in "meta-level" thinking is hypothesized to lead to more constructive learning. This notion is similar to the argument made by Ohlsson (2011) in relation to strategy shifting and 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 describe their experiences as they worked through a novel video game. They plant that participants who engaged in video games regularly were more able to describe their problem-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 not at an impasse. The research on impasse-driven learning again suggests that there is something critical most the metacognitive, learning or study strategies that students engage in when their learning process is disrupted or challenged in some way.

At the cadre 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 education, rooted in Bruner'southward (1961) notion of discovery-based learning as well share this feature. Discovery-based models of education and learning such equally problem-based learning typically present students with an ill-structured scenario, situation or trouble, which they discuss, often in groups, and investigate in order to resolve. Students, in discussing the problem amongst themselves with or without a more than expert facilitator, inevitably run into fabric that they exercise not empathise, that is confusing, and represents an impasse in their investigation of the problem. These impasses are cardinal to the problem-based learning instructional model as they both drive the learning process (condign the "learning issues" that guide students' learning and guide their investigations of the problem) and they also are said to act as intrinsic motivators for students as they endeavour to resolve the trouble (Schmidt, 1993).

Given some of the cadre similarities between these theoretical models,—productive failure, impasse driven learning, desirable difficulties, and trouble-based learning—a key question for educational researchers is: what are the underlying cognitive and learning processes that both bring about educatee confusion, and underpin the potential learning benefits derived from it? Besides, how do these processes differ between private students, learning different material, and engaged in different types of tasks? Graesser and D'Mello (2012) suggest that the prime candidate for this underpinning process is cerebral disequilibrium. The notion of cerebral disequilibrium is derived from Piaget's work on cognitive development (Piaget, 1964). It occurs when there is an imbalance created when new data does non seamlessly integrate with existing mental schema. It is plausible then that defoliation is the result of certain types of difficulties in the learning process, namely those that lead to an impasse underpinned by cognitive disequilibrium. In attempting to blueprint for and provide interventions for productive challenges and so, what appears to be important is non the introduction of difficulties per se but the introduction of difficulties that lead to an impasse and a sense of disequilibrium. Based on the inquiry across these domains this, in plow, is hypothesized to pb to a change in learning approach or problem-solving strategy that tin can heighten learning.

A Framework for Agreement and Seeing Difficulties and Resulting Confusion in Learning

From this review, it seems articulate that difficulties experienced during learning and resulting in defoliation can be either productive or unproductive depending on the arrangement of and relationship between a range of variables within a learning surroundings. These include the type of learning action, the knowledge domain being learned, and individual 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 by a learner, and whether the feel of the resulting epistemic emotion volition be productive or unproductive, is a issue of a circuitous relationship between:

(i) Individually-based variables, such as prior knowledge, self-efficacy, and self-regulation;

(two) 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 activity or task.

A key challenge 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 besides suggests two learning processes could be promoted when students experience confusion: one general and one specific. The beginning, more full general, process 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 defoliation that has resulted from it. The 2nd is that students, when piqued by a conceptual impasse and the resulting feelings of confusion, actively generate and adopt alternative approaches to the learning material they are seeking to empathise. This second process suggests that students practice not but invest a greater effort in their learning; it suggests that they investigate and prefer alternative study approaches and strategies, which they then utilise. In order for this second process to occur, students need to be sufficiently able to monitor their progress and sympathize how to take activity on the basis of their experience of difficulty or the reaching of an impasse.

Finally, this review suggests that insurmountable learning difficulties may ascend when students experience likewise much confusion or when defoliation persists for too long. As discussed by D'Mello and Graesser (2014) one of the nigh important factors in the benign effect of confusion is that it is resolved. Unresolved, persistent defoliation leads to frustration, colorlessness and therefore is detrimental for learning. In an case of this frail balance in action, Lee et al. (2011) examined confusion while novices attempted to learn how to write computer lawmaking. They institute that overcoming confusion tin enhance learning but, when information technology remains unresolved, it leads to deleterious effects on student achievement. This observation speaks to the importance of addressing student confusion in a timely and personalized way. Notwithstanding, given the complexities introduced by the individual differences between students, this is not a straightforward task.

In many ways, these features of confusion 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 not to accept besides little or besides much difficulty but to aim to accept just the right amount. If educators and educational designers aimed to create challenges and induce a change in learning strategy as a deliberate tactic to promote conceptual change, students would need to experience sufficient subjective difficulty for the impasse in the learning process to be experienced every bit defoliation. However, if besides much or persistent confusion is experienced, information technology will lead to frustration, hopelessness, boredom and giving upwardly. To apply difficulties equally a deliberate instructional strategy in digital learning environments is, therefore, a double-edged sword. If students are non sufficiently engaged to get confused and redress their manner of approaching the activity, they can and so get bored and potentially regress back to their initial conception. If students can exist guided and supported through their defoliation, nevertheless, information technology tin can and so pb to the productive learning outcomes reported in the empirical literature. That, in essence, is the ZOC.

One ongoing consequence with the notion of "optimal defoliation" is that information technology is difficult to make up one's mind what separates productive from non-productive confusion as learning unfolds. Given the complexities involved due to individual responses to difficulties in learning, the threshold at which effective confusion becomes non-productive frustration or colorlessness will differ markedly between individuals (Kennedy and Order, 2016). Identifying where a student might be forth the confusion continuum in advance of knowing the outcome of the learning activity is a pregnant challenge. Kennedy and Gild constitute that there were markers axiomatic in trace data suggestive of students crossing the threshold into unproductive forms of confusion. For case, extended delays in progress observed as significant fourth dimension 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 simply there is some emerging show that information and analytics could be used to aid 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 mechanism for the important role of confusion in learning, nosotros propose a framework for confusion in digital learning environments (see Figure 1). From the top of Figure 1, a learning event can be specifically designed to create cognitive disequilibrium. An example of this is the approach used by Muller et al. (2008) to create disequilibrium in videos. In this study, the researchers created disequilibrium past focussing on misconceptions as a core instructional strategy, the disequilibrium being generated through the distance betwixt what people retrieve they know and the accepted scientific agreement. From in that location, disequilibrium is generated equally a crusade of an impasse in the learning process. At this stage, students volition move into the ZOC then long equally they are sufficiently engaged and aspect the impasse to exist confusing. If this occurs in a productive style and the student has sufficient metacognitive awareness and skill to recognize the confusion as a cue to modify strategy, the disequilibrium will be effectively resolved, conceptual alter will occur, and students will move on to some other learning event. If the confusion becomes persistent, on the other hand, and so students may maybe motility into the zone of sub-optimal confusion (ZOSOC). When this occurs, the confusion becomes unproductive and leads to possible frustration and/or colorlessness. Once more, it 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 by D'Mello and Graesser (2014) but is particularly focused on further elucidating both the underpinning processes and the characteristics of the learning design that might influence both the initiation of defoliation and its resolution.

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

Implications of the Framework

If it tin exist assumed that confusion is benign for learning under some circumstances and so it is worth because the implications of this for learning blueprint. The creation of disequilibrium and defoliation is important to both engage students and create the uncertainty required to assistance them develop conceptual cognition. A learning effect 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 non enter the ZOSOC. Enticing students to enter the ZOC has been achieved in numerous ways as described above. For example, the material presented or the medium through which it is presented can be contradictory, counterintuitive or the surround tin can take piffling to no guidance every bit in pure discovery-based learning and, to a lesser extent, productive failure. Taken together, there would appear to be many means 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 job with renewed vigor rather than attribute an impasse equally disruptive (Arguel et al., 2016).

When information technology does occur, ensuring the confusion leads to a productive outcome is more challenging equally information technology requires the students themselves resolving the disequilibrium, a timely intervention from a teacher, or in a way that can be automatically supported in a digital learning environs. Thus, there appear to be two broad options for ensuring confusion leads to productive outcomes. Equally alluded to to a higher place, the development of effective self-regulation in learning is ane way of ensuring that students motility from being confused to finer learning. While students' skills in self-regulation are something they may at least partly bring to a learning event, there is likewise potential for building in interventions to help with self-regulation into the learning environment (Lodge et al., 2018). For example, if students did change their strategy or approach to a learning result, this creates an opportunity for them to reflect on the change in their approach and consider how such a strategy might exist useful in futurity learning situations. So, while there are opportunities for helping students to finer larn new material, there are also possibilities in these situations for students to consider the strategies they utilise when learning more broadly. In a very concrete way, one intervention strategy is to help students to understand that difficulties and confusion as part of the learning process are perfectly normal and, indeed, necessary in many instances. Helping students to see confusion every bit a cue to try a dissimilar arroyo rather than come across information technology is a sign that they are incapable would be a simple manner to improve students' chapters to bargain with difficult and confusing elements of learning.

A 2nd option for ensuring that students finer pass through the ZOC and achieve productive learning outcomes is to use feedback. Feedback tin take many different forms in digital learning environments thus providing many options for intervening when students appear to exist confused. The critical attribute of any intervention on defoliation to avert having students enter into the ZOSOC will be to personalize that feedback by taking into account their prior knowledge (Lehman et al., 2012). Intelligent tutoring systems accept some capacity for this level of personalisation. However, much remains to be done earlier these systems tin can be regarded equally being truly adaptive to the affective components of student learning and practical at scale (Baker, 2016). Every bit a proof of concept though, there are examples of sophisticated adaptive systems that accept been built to provide real time feedback and prompts based on educatee operation equally they progress through procedural tasks. For example, adaptive systems have long been available to provide data-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 use data about educatee interaction to inform feedback interventions suggest that it is possible to build systems that will piece of work across different knowledge domains to answer to students having difficulties.

In the interim, while intelligent tutoring and other adaptive systems congenital on machine learning and artificial intelligence mature, there are possibilities for building digital learning environments to cater for difficulties and resulting confusion. Most prominent amid these are the development of sophisticated learning designs that tin can answer to student defoliation through enhancing student self-regulation and providing feedback in the course of hints or formative information about the strategies or approaches being used. That is not to say that the evolution of such systems will exist easy. Part of the arroyo to helping students get ameliorate equipped to deal with difficulties and confusion needs to be to address the notion that difficulties are inherently detrimental and an indicator that students are not capable.

Conclusion

Difficulties and the defoliation that ofttimes results are difficult to detect, manage, and answer to in digital learning environments and large classes compared to smaller group face-to-face settings. Despite this, in this paper nosotros have argued that difficulties and defoliation are important in the process of learning, specially when students are developing more than 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 confusion could be beneficial for learning. The creation of a sense of cognitive disequilibrium appears to be a vital element and the confusion needs to be effectively resolved by helping students pass through the ZOC without them entering the ZOSOC. Nosotros have attempted hither to provide a conceptual model for the process by which students pass through this optimal zone. Our promise is that this will assist to outline the process of the evolution and resolution of confusion so that researchers and learning designers can go on to develop methods for ensuring students attain productive outcomes every bit a event of condign confused.

Author Contributions

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

Funding

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

Conflict of Interest Statement

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

Acknowledgments

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

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