Once Again Another Author Confises Learning With Recalling

Introduction

As grade sizes in instruction are increasing and applied science is impacting on education at all levels, these trends create meaning challenges for teachers as they attempt to support individual students. Technology undoubtedly provides substantial advantages for students, enabling them to access information from effectually the planet easily and at any time. The advantages and disadvantages of the increased use of engineering take come to low-cal over time as students increasingly engage with new innovations. In this review, nosotros will address an effect that has become progressively evident in digital learning environments but is relevant to all educational settings, particularly as grade sizes grow. Nosotros will explore the difficulties in attempting to understand and business relationship for the struggles students experience while learning a detail emphasis on what happens when students experience difficulties and become dislocated.

Running into problems while learning is often accompanied by an emotional response. Emotion, more broadly, plays a vital role in the integration of new knowledge with prior noesis. This has been establish to be the case in brain imaging studies (e.g., LeDoux, 1992), laboratory-based studies (e.g., Isen et al., 1987), and applied educational studies (east.g., Pekrun, 2005). A clear case of how emotion tin bear on on the learning process is where information technology creates an obstacle to learning, reflected in, for example, 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 Club, 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 face up-to-face human interaction in digital environments, emotions like confusion are difficult to observe. Information technology is therefore challenging to respond to students with support or feedback to help their progress when they are stuck and become dislocated. Humans are uniquely tuned to respond to the emotional reactions of other humans (Damasio, 1994). Intuitively we know what it is like to experience confused equally a issue of a difficulty in the learning process, even so confusion is not regarded as one of the "bones" emotions: like, for instance, happiness, sadness, and anger (Ekman, 2008). And while student defoliation is relatively easy for an experienced teacher to detect in face-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 nosotros know that confusion is both commonly felt by students, is able to be diagnosed by teachers, and able to be resolved productively with instructor support (see 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 exact relationship between difficulties and emotional responses in learning. Thus, student emotions, such as confusion, are relatively straightforward for experienced teachers to detect, understand and respond to in face up-to-face settings with relatively small class sizes (run into Woolfolk and Brooks, 1983; Woolf et al., 2009; Mainhard et al., 2018). The same is not truthful in digital environments or large classes. Emotions are less obvious to teachers when there are many students or when they interact with students via electronic methods (Wosnitza and Volet, 2005). This ways 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 equally new technologies and increasing class sizes bear upon pedagogy. Digital learning environments, specially online or distance learning environments, are often explicitly designed so that students volition take flexibility and autonomy in their studies. Students, when studying online or at a altitude, are oft able to access course fabric and resources in their own time (and place) and are often not constrained past centralized timetables. As a event, 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 often leads to students having fewer opportunities for engaging with teaching staff and receiving feedback in existent time (Mansour and Mupinga, 2007). While activities can be made available in the form of webinars and other synchronous formats, there remains a substantial responsibleness on students to exist autonomous and make expert decisions about their ain progress without requiring the real-fourth dimension intervention of teaching staff.

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

In club for a digital learning environs to be responsive to difficulties—or indeed to other emotions that impact on learning—it is necessary for the system to detect the emotions that students experience during their learning (Arguel et al., 2017). These emotional responses are the central indicator teachers apply in face-to-face up settings to make up one's mind when students are having issues. Given the difficulty of identifying emotions in digital learning environments in means that humans tin in contiguous environments, this is a particularly vexing issue and 1 that has led to the growth of the burgeoning field of affective calculating (Picard, 2000). A 2d requirement is that digital learning environments need to be reactive to emotional responses such equally confusion once these responses have been detected. For example, it would exist useful if confused learners were given organization-generated, programmed support to help them resolve their difficulties within the environment itself. Without a teacher present and without whatsoever automated support, it is possible that a pupil may succumb to their confusion, get frustrated and, equally a effect, disengage entirely (D'Mello and Graesser, 2014). While it is hard enough to determine when students become confused in these environments, it is even more complex to know when and how to intervene to prevent the confusion from becoming colorlessness or frustration. Finally, it would be a distinct reward if whatever response or feedback that a digital learning environment provided a confused educatee could be tailored and personalized to the individual educatee and their learning pathway, progress and process (Lodge, 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 can help them manage both their emotions and their approach to the particular learning action they are finding disruptive. Effective intervention represents a pregnant challenge for designers of digital learning environments as teachers are adept at responding to student emotions in nuanced and personalized means that are non easily programmed into a digital system.

Taken together, it is apparent that the increased utilize of digital learning environments has created a need for better understanding and intervening when students experience difficulties and become confused. This situation is, however, not helped by ongoing conjecture in the literature equally to whether difficulties in the learning procedure resulting in confusion are detrimental or beneficial for learning (Arguel et al., 2017). For case, Dweck (1986) argues that defoliation is consistently detrimental to learning and is mediated past prior achievement, IQ scores, and conviction. She suggests that students who accept poor prior achievement and confidence are at take chances of attributing the experience of reaching a learning impasse and their resulting emotional response to their lack of aptitude. That is, students who become confused while completing a learning activeness may interpret their defoliation as a sign that they are incapable of learning the material. This argument aligns with a body of literature showing that persistent confusion tin lead to frustration and boredom, which as a issue has a negative impact on learning (D'Mello and Graesser, 2014). More recently, however, research has suggested that difficulties resulting in defoliation can benefit student learning. This is perchance best exemplified in the research on what have been labeled "desirable difficulties" (Bjork and Bjork, 2011), specific features of the learning situation that introduce beneficial difficulties that reliably enhance learning. Forth similar lines, D'Mello et al. (2014) found that inducing difficulties and confusion in an intelligent tutoring system appeared to enhance learning. Moreover, some research has indicated that difficulties may be particularly beneficial for conceptual learning, where students sometimes need to overcome misconceptions before developing a more than sophisticated understanding of the topic surface area (Kennedy and Lodge, 2016). For example, Chen et al. (2013) developed a predict-observe-explain activity near usually misconceived notions in electronics. Conflicting information was presented to students in the form of scenarios and the resulting confusion, when resolved, appeared to heighten student learning, particularly in relation to correcting the misconceptions. What is apparent from this research is that there seems to exist a complex mix of factors that lead to students experiencing difficulties and doubtfulness most what kinds of outcomes occur as a result. The factors vary between students and the kinds of difficulties faced will differ beyond knowledge domains and job types.

From these few studies it is evident that experiencing difficulties and confusion might be beneficial for different students nether different circumstances and that the part of confusion in productive learning is important to understand across different learning environments, knowledge domains, and types of learning activities. Dweck'south (1986) work indicates that defoliation may exist interpreted, managed and adapted to in different ways past students depending on their levels of confidence and past achievements. On the other manus, the work of D'Mello et al. (2014) and Chen et al. (2013) suggests that confusion can aid students' learning, especially when conceptual learning or conceptual change is the aim of the activity.

In this integrative review, we examine the literature on difficulties in learning. Nosotros focus here on the ways in which it might exist possible to detect confusion experienced as a outcome of difficulties and arbitrate when students are counterproductively dislocated. Our aim is to explore the ways in which the difficulties students experience in learning could exist harnessed for the purpose of enhancing their teaching. If digital learning environments are to achieve their potential, they must be designed in a way to enable sophisticated support and feedback to confused students, in ways that are similar to those a teacher can provide in small group contiguous settings.

Difficulties, Defoliation, and Their Function in Learning

While confusion is mutual in educational practice and learning research, generally speaking, it has been poorly defined and understood in the educational literature (Silvia, 2010). Confusion is often associated with reaching a cognitive impasse or "being stuck" while trying to acquire something new (Woolf et al., 2009), and it is besides normally regarded every bit a negative emotional feel or something to be avoided while learning ("Miss, help me, I am dislocated!"; run into 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 defoliation is detrimental to learning. While in that location is certainly research that suggests when defoliation persists to the point of frustration, information technology usually leads to negative outcomes and has a detrimental impact on understanding (Dweck, 1986; D'Mello and Graesser, 2011), as mentioned in a higher place, at that place are times when information technology may exist beneficial to experience a cognitive impasse and the feeling of defoliation when learning.

When information technology comes to defining what confusion really is, there has been some ambivalence equally to the extent to which it is a cerebral or emotional phenomenon (D'Mello and Graesser, 2014). This doubt stems from debates about whether or non emotions such as confusion require some element of estimation in order for the subjective feel of the emotion to take grade. These views are derived from an attributional perspective on emotion (Schachter and Vocalizer, 1962). The process, co-ordinate to this perspective, is that confusion is the result of an individual's attribution of an melancholia response to a preceding subjective experience. In other words, the educatee reaches an impasse that causes them some difficulty. As a result of the impasse, the educatee has some sort of emotional response to the situation they find themselves in. That emotional response is then interpreted past the individual—they aspect significant to it—which may be confusion (or feet, or excitement). In this style, the private experiences or "attributes" the emotion of confusion to the impasse. This estimation is particularly important given that confusion in learning needs to be nearly some educational material attempting to be understood past a student (Silvia, 2010). Withal, the attributional process likewise suggests that there are substantial differences between individuals in terms of the attributions they make. Two students tin can experience the exact same educational conditions and interpret them in vastly different ways, leading ane to be confused while the other experiences no such response. The interaction between subjective experience and content knowledge has led to defoliation existence defined as an "epistemic emotion" (Pekrun and Stephens, 2011). In other words, confusion tin can exist defined as an melancholia response that occurs in relation to how people come to know or understand something. When divers every bit an epistemic emotion, confusion is considered to have both cognitive and affective components.

While it is reasonably clear that confusion has both cognitive and melancholia components, what is less obvious is whether difficulties in learning that effect in confusion are productive or unproductive in learning. The literature in this expanse is somewhat equivocal. D'Mello et al. (2014) examined students when learning virtually scientific reasoning using an intelligent tutoring system. Past inducing confusion through the presentation of contradictory data, they were able to decide whether the feel of being dislocated contributed negatively or positively to learning outcomes. 2 virtual agents were used in the intelligent tutoring system to present information about the topic. In the confusion condition, 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.e., contradictory) condition compared to when they completed the control (i.due east., non-contradictory) status they showed enhanced functioning, and every bit a result, argued that defoliation tin exist benign 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 conditions it contributes productively to student learning outcomes (e.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' confusion (and other emotions) during an action in a digital learning environs that focussed on fractions. They found that, when provided with the appropriate support at the right time, in the grade 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 student 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 confusing aspects of the concepts at the commencement 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 noesis 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 particularly Muller et al.'due south (2007) estimation of their results, suggests that, when students experience difficulties and confusion, it may in fact serve as a trigger to assist them overcome any conceptual obstacles they encounter during their learning. Along similar lines, Ohlsson (2011) argues that impasses and difficulties experienced in the learning process could be effective triggers for students to rethink their learning approaches. When students reach a conceptual impasse, this may serve equally a cue that their current strategy or approach to the learning material is not constructive, leading them to consider alternate strategies (D'Mello and Graesser, 2012). This perspective is consequent with enquiry that has considered students' strategies for dealing with challenging material. In a series of experimental studies, Modify et al. (2007) found that, when difficulties are introduced while people acquire and reason about new information, it triggers a shift in strategy, activating a more than systematic or analytic arroyo to the fabric. It may be, therefore, that difficulties encountered during the learning process that are accompanied by a subjective feeling of defoliation can pb students to change their learning strategies which may resolve the impasse, resulting in learning benefits. What this research and the findings suggest, nevertheless, 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 have found that defoliation-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 example, Andres et al. (2014) examined confusion while students engaged with a problem solving-based video game designed to assistance them learn nigh physics. In this report, confusion negatively impacted on students' ability to solve the issues and, compared to students who were less confused, confused students were less probable to primary the learning material. A second study, Poehnl and Bogner (2013), presented culling scientific conceptions to a big group of ninth grade students. Despite the apparently higher levels of confusion in this grouping 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. Equally such, there is conflicting evidence near what role difficulties and resulting defoliation play in learning nether different conditions. Given the possibility that confusion 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 written report may not have the same capacity to cocky-regulate their learning as university students in the other studies discussed here.

Perhaps surprisingly, these are amongst the few empirical investigations to straight consider the touch on of confusion on students' learning that have found information technology has a deleterious effect and those that take often involve younger students. All the same, research from other areas of learning and didactics, while not direct because the role of confusion in learning, accept provided findings that are relevant to the role that difficulties and confusion may play in students' learning. The important distinction seems to be the difference between difficulties that students experience and the emotions that they experience as a result of these difficulties. While there has been express research examining students' experiences of confusion, there has been much work done on trying to understand the role of difficulties in the learning process. For this review, we scanned the literature in educational psychology, experimental psychology, and education to expect for concepts that share a family resemblance (as 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.one thousand., Bjork and Bjork, 2011), productive failure (due east.thou., Kapur, 2008), impasse-driven learning (east.yard., VanLehn, 1988), cognitive disequilibrium (eastward.g., Graesser et al., 2005), and investigations of learning in discovery-based environments (e.g., Moreno, 2004; Alfieri et al., 2011). It is amidst these cognate fields of research that nosotros may notice further testify to back up the processes that atomic number 82 to confusion being beneficial (or not) for learning. Our aim in attempting to compare and contrast this literature is to better sympathise how difficulties and confusion may exist beneficial to learning and under what conditions.

Studies of desirable difficulties typically consider how aspects of the learning process can encumber learners, and how this process (or "difficulty") 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 conditions where the words were presented upright. In a similar written report 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 environs. Across these studies, Adams et al. found that the utilise of erroneous examples in mathematics instruction led to improvements in learning consistent with those observed in the broader literature on desirable difficulties. In order to describe the mechanism by which difficulties enhance learning, Adams et al., argue that the use of wrong examples encourages students to process 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 problem solutions, procedure textile more deeply and this is idea to be responsible for the enhanced learning observed (encounter also McDaniel and Butler, 2011).

The growing body of inquiry on desirable difficulties has raised some questions about what constitutes a beneficial difficulty in the learning process (Yue et al., 2013). For example, in a widely cited written report, Diemand-Yauman et al. (2011) presented material to participants (study 1) and students (study 2) in easy and hard to read fonts. They establish that participants and students who studied material in hard to read fonts performed meliorate when later quizzed on the cloth. 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 (e.g., Rummer et al., 2016), creating further uncertainty about what constitutes a desirable difficulty. Whatever the boundary conditions of desirable difficulties, it is credible that certain kinds of difficulties in the learning process tin reliably enhance the encoding, storage and retrieval of information. Participants exposed to desirable difficulties in the majority of the research on these effects to engagement take washed so predominantly under laboratory weather. Withal, it is apparent that in that location were substantial advantages to introducing targeted difficulties in the learning process that are strong candidates for enhancing learning in alive 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 use of difficulties to raise learning. Productive failure is a manner of sequencing learning activities to give students an opportunity to familiarize themselves with a complex problem or issue in a structured surroundings but without pregnant instruction 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 subsequently beingness given explicit instruction on the procedure associated with how to solve the problems. He found that the grouping of students who were given the opportunity to attempt problems before being given explicit instructions, despite oft failing in their starting time 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 reach 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 problem-solving strategies through a procedure that enhances learning over both the shorter and the longer term. It should be noted hither that the tasks used in productive failure studies are different to those used in studies of desirable difficulties. Studies on productive failure tend to employ more realistic issues given to students rather than tasks that rely more on memorisation.

Despite the different kinds of tasks used, there are clear parallels between the "failure" aspect of productive failure, and the "difficulties" encountered by students within a desirable difficulty paradigm (Kapur and Bielaczyc, 2012). In both situations, at that place is a deliberate strategy to encumber students' learning process and potentially trigger defoliation. Dissimilar the work on desirable difficulties, nevertheless, much of the enquiry on productive failure has been carried out in naturalistic educational settings. This is achieved partly through the sequencing of the activity. The lack of direct education on the problem or issue often leads students to inevitably reach an impasse in the learning procedure that is seemingly accompanied past a sense of defoliation (Hung et al., 2009). Every bit summarized by Kapur (2015), the benefits of productive failure take been demonstrated many times in the peer-reviewed literature (due east.thou., 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 reach an impasse (i.e., the process leads to failure), it leads to enhanced learning in educational situations that are designed to rely on straight didactics.

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 employ trouble-solving strategies to overcome the impasse or seek help. In both cases, the necessity of engaging in "meta-level" thinking is hypothesized to lead to more effective learning. This notion is like to the argument fabricated past 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 equally 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 procedure applies to tutoring, VanLehn et al. (2003) analyzed dialogue in tutoring sessions on physics. Their results suggested that students were receptive to tutoring specially when they reached an impasse in the learning procedure compared to when they were not at an impasse. The research on impasse-driven learning again suggests that there is something critical almost 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 teaching, rooted in Bruner'due south (1961) notion of discovery-based learning besides share this characteristic. Discovery-based models of didactics and learning such as problem-based learning typically nowadays students with an sick-structured scenario, situation or trouble, which they hash out, oft in groups, and investigate in lodge to resolve. Students, in discussing the problem among themselves with or without a more expert facilitator, inevitably come across material that they do not empathize, that is disruptive, and represents an impasse in their investigation of the trouble. These impasses are cardinal to the problem-based learning instructional model as they both bulldoze the learning process (becoming the "learning issues" that guide students' learning and guide their investigations of the problem) and they likewise are said to act as intrinsic motivators for students as they attempt to resolve the problem (Schmidt, 1993).

Given some of the core similarities betwixt 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 student defoliation, and underpin the potential learning benefits derived from information technology? Besides, how practise these processes differ between individual 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 cognitive disequilibrium. The notion of cerebral disequilibrium is derived from Piaget's work on cerebral evolution (Piaget, 1964). It occurs when there is an imbalance created when new data does not seamlessly integrate with existing mental schema. It is plausible so that confusion is the outcome of certain types of difficulties in the learning procedure, namely those that pb to an impasse underpinned by cognitive disequilibrium. In attempting to pattern for and provide interventions for productive challenges then, what appears to be of import is not the introduction of difficulties per se but the introduction of difficulties that atomic number 82 to an impasse and a sense of disequilibrium. Based on the enquiry beyond these domains this, in turn, is hypothesized to lead to a change in learning arroyo or trouble-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 confusion tin be either productive or unproductive depending on the system of and human relationship between a range of variables inside a learning environment. These include the type of learning activity, the knowledge domain being learned, and individual differences such equally how students attribute difficulties and their capacity for self-regulated learning. For any particular learning or content surface area, the caste to which difficulties are experienced by a learner, and whether the experience of the resulting epistemic emotion will be productive or unproductive, is a result of a complex human 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 claiming for educational researchers is to determine what sets of relationships betwixt what variables lead to adaptive and maladaptive learning processes and outcomes in digital learning environments.

The review of the literature also suggests two learning processes could be promoted when students feel defoliation: one general and ane specific. The first, more general, process is that difficulties encourage students to invest more "mental try" in their learning; they somehow piece of work harder cognitively—through attention or concentration—to resolve the conceptual impasse and the defoliation that has resulted from it. The second is that students, when piqued past a conceptual impasse and the resulting feelings of defoliation, actively generate and adopt alternative approaches to the learning material they are seeking to empathise. This second process suggests that students exercise not simply invest a greater effort in their learning; it suggests that they investigate and adopt alternative report approaches and strategies, which they then apply. In order for this second process to occur, students need to be sufficiently able to monitor their progress and understand 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 arise when students experience too much confusion or when confusion persists for too long. As discussed by D'Mello and Graesser (2014) ane of the nearly of import factors in the benign effect of confusion is that it is resolved. Unresolved, persistent confusion leads to frustration, colorlessness and therefore is detrimental for learning. In an case of this frail residuum in action, Lee et al. (2011) examined defoliation while novices attempted to larn how to write computer code. They found that overcoming confusion can raise learning but, when information technology remains unresolved, information technology leads to deleterious effects on student achievement. This observation speaks to the importance of addressing student confusion in a timely and personalized way. However, given the complexities introduced by the individual differences between students, this is non a straightforward job.

In many ways, these features of confusion are captured in Graesser's (2011) notion of a "zone of optimal confusion" (ZOC). Reminiscent of Vygotsky's (1978) concept of the zone of proximal evolution, the ZOC suggests that it is important not to have too piffling or too much difficulty but to aim to have just the right amount. If educators and educational designers aimed to create challenges and induce a change in learning strategy every bit a deliberate tactic to promote conceptual modify, students would need to experience sufficient subjective difficulty for the impasse in the learning process to exist experienced as confusion. All the same, if too much or persistent confusion is experienced, it will atomic number 82 to frustration, hopelessness, colorlessness and giving upward. To apply difficulties as a deliberate instructional strategy in digital learning environments is, therefore, a double-edged sword. If students are not sufficiently engaged to become dislocated and redress their way of approaching the activity, they tin and so go bored and potentially regress dorsum to their initial conception. If students can be guided and supported through their defoliation, however, it can then lead to the productive learning outcomes reported in the empirical literature. That, in essence, is the ZOC.

1 ongoing issue with the notion of "optimal confusion" is that it is difficult to determine what separates productive from non-productive defoliation 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 boredom will differ markedly between individuals (Kennedy and Lodge, 2016). Identifying where a pupil might be forth the confusion continuum in advance of knowing the effect of the learning activeness is a significant challenge. Kennedy and Lodge found that there were markers axiomatic in trace data suggestive of students crossing the threshold into unproductive forms of defoliation. For example, extended delays in progress observed every bit significant time lags betwixt interactions or rapid cycling through activities are possible indicators of boredom or frustration respectively. Inferring in existent time whether students are experiencing confusion that is productive or unproductive remains a challenge simply there is some emerging testify that information and analytics could be used to help predict how students are tracking and provide feedback and support independent of knowing the consequence (Arguel et al., 2019).

Based on Graesser'southward (2011) "ZOC" and, using cognitive disequilibrium as a framing mechanism for the important part of confusion in learning, we suggest a framework for confusion in digital learning environments (come across Figure 1). From the top of Effigy one, a learning event can exist 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 study, the researchers created disequilibrium by focussing on misconceptions as a cadre instructional strategy, the disequilibrium being generated through the altitude between what people think they know and the accepted scientific agreement. From there, disequilibrium is generated as a crusade of an impasse in the learning process. At this stage, students will move into the ZOC and so long as they are sufficiently engaged and aspect the impasse to be confusing. If this occurs in a productive way and the educatee has sufficient metacognitive awareness and skill to recognize the confusion as a cue to change strategy, the disequilibrium will be finer resolved, conceptual alter will occur, and students will move on to another learning event. If the confusion becomes persistent, on the other paw, so students may mayhap movement 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 decide in real time when and how this occurs and that remains a challenge for future research to examine. The model proposed here builds on like 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 confusion and its resolution.

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

Implications of the Framework

If it can be causeless that confusion is beneficial for learning under some circumstances so it is worth considering the implications of this for learning pattern. 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 textile presented or the medium through which information technology is presented can be contradictory, counterintuitive or the environment can have little to no guidance every bit in pure discovery-based learning and, to a lesser extent, productive failure. Taken together, there would appear to exist many ways to lure students into the ZOC. That said, in that location are no guarantees that students will enter this ZOC. If a student has high levels of prior cognition or is highly confident, for example, they may persist at a job with renewed vigor rather than aspect an impasse as confusing (Arguel et al., 2016).

When information technology does occur, ensuring the confusion leads to a productive outcome is more challenging as it 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 environment. Thus, there appear to be two broad options for ensuring confusion leads to productive outcomes. As alluded to above, the development of effective self-regulation in learning is one fashion of ensuring that students movement from existence confused to effectively learning. While students' skills in self-regulation are something they may at least partly bring to a learning event, at that place is also potential for building in interventions to assistance with self-regulation into the learning environment (Guild et al., 2018). For example, if students did alter their strategy or approach to a learning event, this creates an opportunity for them to reflect on the change in their approach and consider how such a strategy might be useful in future learning situations. So, while there are opportunities for helping students to effectively learn new material, there are too possibilities in these situations for students to consider the strategies they use when learning more than broadly. In a very concrete mode, one intervention strategy is to assistance students to sympathise that difficulties and confusion every bit part of the learning process are perfectly normal and, indeed, necessary in many instances. Helping students to come across defoliation as a cue to try a different approach rather than see it is a sign that they are incapable would be a simple way to improve students' capacity to bargain with hard and confusing elements of learning.

A second choice for ensuring that students effectively pass through the ZOC and achieve productive learning outcomes is to use feedback. Feedback can accept many dissimilar forms in digital learning environments thus providing many options for intervening when students appear to exist confused. The critical aspect of any intervention on defoliation to avert having students enter into the ZOSOC will be 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. However, much remains to be done before these systems can be regarded every bit being truly adaptive to the affective components of student learning and applied at calibration (Baker, 2016). Equally a proof of concept though, at that place are examples of sophisticated adaptive systems that have been built to provide real time feedback and prompts based on student operation 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 tin can use data about student interaction to inform feedback interventions advise that information technology is possible to build systems that will work across dissimilar knowledge domains to reply to students having difficulties.

In the interim, while intelligent tutoring and other adaptive systems built on machine learning and artificial intelligence mature, in that location are possibilities for edifice digital learning environments to cater for difficulties and resulting confusion. Most prominent among these are the development of sophisticated learning designs that tin respond to pupil defoliation through enhancing student self-regulation and providing feedback in the course of hints or determinative information virtually the strategies or approaches being used. That is not to say that the development of such systems volition exist easy. Part of the approach to helping students become better equipped to deal with difficulties and confusion needs to exist to address the notion that difficulties are inherently detrimental and an indicator that students are not capable.

Conclusion

Difficulties and the confusion that often results are difficult to detect, manage, and respond to in digital learning environments and big classes compared to smaller group face-to-face up settings. Despite this, in this paper nosotros take argued that difficulties and confusion are important in the process of learning, particularly 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 as to how confusion could exist beneficial for learning. The creation of a sense of cerebral disequilibrium appears to be a vital chemical element and the confusion needs to be finer resolved by helping students laissez passer through the ZOC without them entering the ZOSOC. We have attempted here to provide a conceptual model for the process by which students pass through this optimal zone. Our promise is that this volition help to outline the process of the development and resolution of confusion so that researchers and learning designers tin can proceed to develop methods for ensuring students attain productive outcomes equally a result of condign confused.

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 Research Quango for the work in this review as part of a Special Inquiry Initiative (Grant number: SRI20300015).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of involvement.

Acknowledgments

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

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