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Opportunities for Analytics in Challenge-Based Learning
Abstract
This study is part of a research programme investigating the dynamics and impacts of learning engagement in a challenge-based digital learning environment. Learning engagement is a multidimensional concept which includes an individual’s ability to behaviourally, cognitively, emotionally, and motivationally engage in an on-going learning process. Challenge-based learning gives significant freedom to the learner to decide what and when to engage and interact with digital learning materials. In light of previous empirical findings, we expect that learning engagement is positively related to learning performance in a challenge-based online learning environment. This study was based on data from the Challenge platform, including transaction data from 8951 students. Findings indicate that learning engagement in challenge-based digital learning environments is, as expected, positively related to learning performance. Implications point toward the need for personalised and adaptive learning environments to be developed in order to cater for the individual needs of learners in challenge-based online learning environments.
... Whether scoring-based game mechanics based on the logistical features of the health care system can be efficiently used as attributes of learning engagement has yet to be explored. Serious game analytics may open opportunities for a better understanding of engagement in game-based learning environments [31]. In addition, there are no human-computer simulation practices in recently published gaming articles on the delivery of care in health care organizational settings nor are there scoring systems to gamify the logistics of care within a pediatric ED [32], even though the scoring system is the most frequently used gamification element in comparison with badges, leaderboards, avatars, levels, and rewards. ...
... Based on previous studies' usage of the number of initiated tasks and finished tasks as learning engagement attributes [31], we addressed relationships between logistical performance and the following traits, for which a regression model was performed: ...
... The findings in this study indicated that both negative and positive reinforcements were effective in encouraging behaviors in line with the learning goals of the game. Our findings show that all included learning attributes were important variables for predicting logistical performance in serious gaming and that the number of initialized tasks was the most powerful explanatory variable, in line with previous studies [31]. Additionally, via relation analysis, this study indicates that a gamification design of key logistical aspects of the health care production system can lead to learning engagement in serious gaming. ...
Background
Using serious games for learning in operations management is well established. However, especially for logistics skills in health care operations, there is little work on the design of game mechanics for learning engagement and the achievement of the desired learning goals.
Objective
This contribution presents a serious game design representing patient flow characteristics, systemic resource configurations, and the roles of the players based on a real Swedish emergency ward. The game was tested in a set of game-based learning practices in the modalities of a physical board game and an online multiplayer serious game that implemented the same game structure.
Methods
First, survey scores were collected using the Game Experience Questionnaire Core and Social Presence Modules to evaluate the experience and acceptance of the proposed design to gamify real processes in emergency care. Second, lag sequential analysis was applied to analyze the impact of the game mechanics on learning behavior transitions. Lastly, regression analysis was used to understand whether learning engagement attributes could potentially serve as significant predicting variables for logistical performance in a simulated learning environment.
ResultsA total of 36 students from courses in engineering and management at KTH Royal Institute of Technology participated in both game-based learning practices during the autumn and spring semesters of 2019 and 2020. For the Core Module, significant differences were found for the scores for negative affect and tension compared with the rest of the module. For the Social Presence Module, significant differences were found in the scores for the psychological involvement – negative feelings dimension compared with the rest of the module. During the process of content generation, the participant had access to circulating management resources and could edit profiles. The standard regression analysis output yielded a ΔR2 of 0.796 (F14,31=2725.49, P
... Literature on TML has mostly focused on the implementation and analysis of personal and adaptive learning environments, e.g., based on adaptive formative feedback (Hattie and Timperley 2007;Ifenthaler and Gibson 2019). ...
Technology-mediated learning offers new possibilities for individualizing learning processes in order to discover, monitor, and enhance students’ learning activities. However, leveraging such possibilities automatically and at scale with novel technologies raises questions about the design and the analysis of digital learning processes. Process mining hereby becomes a relevant tool to leverage these theorized opportunities. The paper classifies recent literature on individualizing technology-mediated learning and educational process mining into four major concepts (purpose, user, data, and analysis). By clustering and empirically evaluating the use of learner data in expert interviews, the study presents three design patterns for discovering, monitoring, and enhancing students’ learning activities by means of process mining. The paper explains the characteristics of these patterns, analyzes opportunities for digital learning processes, and illustrates the potential value the patterns can create for relevant educational stakeholders. Information systems researchers can use the taxonomy to develop theoretical models to study the effectiveness of process mining and thus enhance the individualization of learning processes. The patterns, in combination with the taxonomy for designing and analyzing digital learning processes, serve as a personal guide to studying, designing, and evaluating the individualization of digital learning at scale.
... Reflecting on the work of game-based learning and assessment over the past 10 years, Kim and Ifenthaler (2019) suggested that the field of game-based learning and assessment can greatly benefit from the application of learning analytics in all areas, from the design process to classroom implementation. However, it is a challenge to implement learning analytics in digital games such that they are grounded in theory and practice, technically sound, and useful for teachers and learners without flashy, cumbersome additional features (Ifenthaler & Gibson, 2019). Still, recent research has seen advances in game-based learning embracing the opportunities of data science (Alonso-Fernández et al., 2019). ...
The purpose of this special section is to collect in one place how data in game-based learning environments may be turned into valuable analytics for student assessment, support of learning, and/or improvement of the game, using existing or emerging empirical research methodologies from various fields, including computer science, software engineering, educational data mining, learning analytics, learning sciences, statistics, and information visualization. Four contributions form this special section, which will inspire future high-quality research studies and contribute to the growing knowledge base of learning analytics and game-based learning research and practice.
... However, a common complaint in many of the studies is the technical difficulties experienced during the implementation of the course and overall communication in the online environment (Moeller et al., 2010; Strømsø, Grøttum, & Lycke, 2007; Valaitis, Sword, Jones, & Hodges, 2005).Despite the maturity of online-remote PBL programs, online CBL programs has not been fully implemented. The current understanding about challenge-based online environments is limited, mostly through learning analytics conducted by the Curtin Challenge, a digital learning platform that provides challenges in English proficiency, career and leadership development(Ifenthaler & Gibson, 2019). Results have been positive; data suggests that learning engagement in an online setting is significantly associated with learning performance. ...
With just 10 years left to the 2030 Agenda, the world is admittedly still behind in achieving the 17 Sustainable Development Goals (SDGs). One possible solution to speed up the Global Goals is the nexus of Challenge-based Learning (CBL) and youth-led innovation. In light of the COVID-19 pandemic, in-person delivery of CBL programs has become impossible. In July 2020, 74 students participated in the O17 Summer Challenge, an online CBL program organized by the Geneva-Tsinghua Initiative to train young innovators for the SDGs. This thesis is a case study research that employed participant-observation and survey data collection methods to investigate the effects of virtual collaboration on innovation, team performance and overall student satisfaction. The findings suggest different levels of team ‘virtuality’ have no impact on team performance but teams across the board scored significantly lower for their “likelihood of success” compared to other evaluation criteria. The thesis applies the lens of CBL and youth-led innovation to discuss these findings and make recommendations for future CBL-related programs, research and policies.
... The assignment was structured through a series of 17 primary tasks and 76 sub-tasks that created artefacts, including research, writing and designbased work. The teacher created the project framework, tasks, sub-tasks and learning outcomes, using the Challenge platform, a web-based, mobile-ready application platform for active digital learning experiences and event-level data collection (Gibson & Jackl, 2015;Ifenthaler & Gibson, 2019) developed at Curtin University. Challenge integrates with Cisco Webex Teams (https://www.webex.com/downloads.html) ...
Problem-solving and collaboration are regarded as an essential part of twenty-first century skills. This study describes a task-focused approach to network analysis of trace data from collaborative problem-solving in a digital learning environment. The analysis framework builds and expands upon previous analyses of social ties as well as discourse analysis and adds new metrics of collaborative learning, problem-solving and personal learning. Using three forms of evidence—actions and use of resources, communications and constructed products—the article outlines and illustrates a framework for characterising individual and team performance on a team project as a basis for documenting individual and team behaviours linked to personal learning, collaboration and team problem solving. This study provides a preliminary demonstration of the effectiveness of network analysis on quantifying and visualising individual-level and group-level performance in computer-mediated collaborative learning.
... The analysis of single action or behavior and the investigation of more complex series of actions and behaviors can elicit patterns of engagement and therefore provide key insights into learning processes (Ge & Ifenthaler, 2017). Ifenthaler and Gibson (2019) report how highly detailed data traces, captured by the Challenge platform, with many events per learning activity and when combined with new input devices and approaches bring the potential for measuring indicators of physical, emotional, and cognitive states of the learner. The data innovation of the platform is the ability to capture event-based records of the higher-frequency and higher-dimensional aspects of learning engagement, which is in turn useful for analysis of the effectiveness and impact on the physical, emotional, and cognitive layers of learning caused or influenced by the engagements. ...
The implementation of assessment features into game-based learning environments is only in its early stages because it adds a very time-consuming step to the design process. The impact on learning and questions toward reliability and validity of technology-based assessment systems are still being questioned. To answer the question of what people are learning from playing games, researchers have been using a variety of methods including external measures, log data capturing in-game actions, and game-related actions beyond the game context. This chapter seeks to identify why research on game-based assessment is still in its infancy, what advances have been achieved over the past 10 years, and which challenges lie ahead for advancing assessment in game-based learning.
Increasingly higher education programs are made learner centred and flexible to face societal changes. Challenge-based learning (CBL) is an educational concept shaping these open and flexible programs. This article aims to articulate a framework for analysing CBL characteristics within and between study components in academic curricula. It contributes to a detailed conceptualisation of CBL and clarity on what CBL implementations consist of. The dimensions and indicators of the framework reflect points of attention for research and evaluation of CBL design and implementation. We argue for variety in CBL characteristics between study components or curricula. Furthermore, we point out how this conceptualisation of CBL opens for research into designing and teaching for multiple domains, and how it contributes to an identification of commonly agreed characteristics of CBL. Recent CBL projects are referenced as an illustration of the approach. The detailed conceptualisation informs debate and development in a nascent field of research. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Student-facing visualisations have attracted increased attention with recent developments in data-driven tools to support individual and group work. Learning analytics dashboards (LADs), a data-enhanced feedback tool that allows students to make sense of their learning by providing insights into their learning behaviours represents one of the prominent examples of this trend. While these visualisation tools are increasingly used to study and enhance students' learning in academic contexts, current research is limited regarding the effects of the LADs in K-12 environments. There is a missed opportunity to empower teams and allow instructors and researchers to understand how teams use the LAD to regulate their learning. In this study, we developed a K-12 LAD for supporting students' collaborative work and evaluated with respect to students' perceived usefulness of the proposed LAD and the association between its use and course performance. The study followed a mixed-methods approach, combining quantitative analysis of log data from the dashboard and qualitative analysis of students’ perceptions using surveys and focus groups. Our results show that different roles within teams have distinguished engagement patterns with the LAD and that the tool improves the collaborative learning experience. We postulate that the implications of this study will aid future research work when investigating the behaviours of teams and optimising their learning using LADs.
Educational process mining (EPM) offers new possibilities to discover, monitor, improve, or predict students' learning processes using data about their learning activities captured in technology-mediated information systems (IS). Although EPM has recently attracted considerable research interest, there is still limited shared knowledge about the distinctive design characteristics of EPM from an integrative perspective. To address this gap, we conducted a systematic literature review to identify EPM characteristics. Building on a technology-mediated learning perspective, we develop a taxonomy that classifies EPM characteristics into four major categories (i.e., purpose, user, input, analysis). We evaluate and refine our taxonomy with ten domain experts, identified three clusters in the reviewed literature, and derived six archetypes of EPM scenarios based on our categorization. Finally, we formulate a novel research agenda to guide researchers in systematizing and synthesizing research on different technological embeddings of EPM in a students' learning process.
Game-based learning is a dynamic field that has recently garnered much interest from different areas. Although game-based learning is not limited to digital games, and has in fact a long-standing history in human learning and development far beyond the times of digitalization, the growing interest in educational settings can also be attributed to technological advances as well as to particular preferences in users’ digital media behavior. If games exert such a fascination, it is only natural to ask whether and how they can be used to successfully promote learning purposes. This edited volume presents a wide-ranging collection of work and findings on game-based learning inside and across various disciplines. This concluding chapter aims to situate the single contributions of the volume within more general reflections on the concept and potential benefits of game-based learning as well as to provide an analysis and synthesis of major themes that have emerged in the previous chapters. Finally, it intends to sketch ideas for future research on game-based learning from an inter- and transdisciplinary perspective.
Research on adoption of innovations has emerged in the 1960s through focusing on six key characteristics, namely, relative advantage compared to current tools or procedures, compatibility with the pre-existing system, complexity or difficulty to learn, trialability or testability, potential for reinvention and use for unintended purposes and observed effects. In addition, there have been five kinds of people involved in the diffusion of innovation defined as innovators, early adopters, early majority, late majority and laggards. This chapter reflects on the characteristics of an innovation and presents ways on how to improve higher education with the adoption of learning analytics. In addition, different roles in the complex process of adoption of learning analytics in higher education institutions are described. The chapter closes with an outlook toward adoption of learning analytics in higher education institutions.
tehnoloģijas izglītībā (angļu educational technologies (EdTech), vācu Technologien in der Bildung, franču technologies dans l'éducation, krievu технологии в образовании) Šis jēdziens attiecas uz jebkuru mācīšanas un mācīšanās formu, kur tiek izmantotas tehnoloģijas un tehnoloģiskie risinājumi. Sākotnēji ar izglītības tehnoloģijām saprata visu, kas var tikt izmantots kādu darbību automatizēšanai (piemēram, kalkulators, dators u.tml.) vai pieejas infomācijai paplašināšanai, ko senāk varēja nodrošināt ar radio un televīzijas starpniecību, bet šobrīd var nodrošināt, izmantojot tiešsaistes tīkla iespējas. Saistītie šķirkļi komunikācijas zinātne mākslīgais intelekts pedagoģija pedagoģija Latvijā Saturs Kopsavilkums Praktiskā un teorētiskā nozīme Saistība ar citām nozarēm Galvenie sastāvelementi Īsa vēsture Pašreizējais attīstības stāvoklis Nozīmīgākās iestādes Svarīgākie periodiskie izdevumi Nozīmīgākie nozares darbinieki Saistītie šķirkļi Tīmekļa vietnes Ieteicamā literatūra Šobrīd jēdziens vairāk tiek attiecināts uz digitālajām tehnoloģijām un tehnoloģiskajiem risinājumiem (datortehnika, digitālie mācību līdzekļi, tiešsaistes materiāli utt.), bet neizslēdz arī citu tehnoloģiju lietojumu. Tehnoloģijas izglītībā tiek saistītas arī ar izglītības procesa tehnisko un organizatorisko nodrošinājumu, kas var būt mācīšanās
This article presents a new framework for unobtrusive observation analytics of knowledge and skills-in-action through continuous collection of data from individuals while they interact with digital assets either as individuals or on problem-solving teams. The framework includes measures of the skill and knowledge areas of collaboration, creativity, personal learning, problem solving, and global sustainability, which are observed during natural production and use of communications, intentional artifacts, and resources in a digital learning space designed for self-directed and team-based learning challenges. The article describes the digital context for data collection and shows some example data and analyses.
Learning design has traditionally been thought of as an activity occurring prior to the presentation of a learning experience or a description of that activity. With the advent of near real-time data and new opportunities of representing the decisions and actions of learners in digital learning environments, learning designers can now apply dynamic learning analytics information on the fly in order to evaluate learner characteristics, examine learning designs, analyse the effectiveness of learning materials and tasks, adjust difficulty levels, and measure the impact of interventions and feedback. In a case study with 3550 users, the navigation sequence and network graph analysis demonstrate a potential application of learning analytics design. Implications based on the case study show that integration of analytics data into the design of learning environments is a promising approach.
In an interactive digital game or gamified e-learning experience, mapping a learner’s progress, problem-solving attempts, self-expressions and social communications can entail highly detailed and time-sensitive computer-based traces that capture the context, actions, processes and products. New educational measurement and analysis considerations are needed to address the challenges of finding patterns and making inferences concerning what someone knows and can do. Methods based in data-mining, machine learning, model-building and complexity theory are discussed as theoretical foundations for dealing with time sensitivity, spatial relationships, multiple layers of aggregations at different scales, and the dynamics of complex performance spaces. Examples of these considerations in game-based learning analytics are presented and discussed in the hope of making a contribution to the development and refinement of theoretical approaches to measurement.
The aim of present study was in line with transformational theory to examine the direct and indirect effect of individual-focused transformational leadership (TFL) on leader-rated Job Performance. Data collected through questionnaires from sample of 202 employees of a national state-owned mobile telecommunications company in China. Afterwards, the hierarchical multiple regressions were adapted to examine the proposed hypotheses. Empirical findings of data provides evidence to support our intended hypothesizes and revealed that individual-focused TFL and Employee Work Engagement (EWE) were significantly related to Job Performance, in which the effect of individual TFL on Job Performance was mediated by the EWE. Practically, indicated that work groups or organizations by stimulating and inspiring leaders who would demonstrate transformational behaviors as a significant prerequisite for employees to be engaged, may be able to increase employees’ Job Performance. This study was one of the first to examine antecedents and consequence of EWE simultaneously. More implications for theory, practices for practitioners and future researches are discussed.
The rise of digital game and simulation-based learning applications has led to new approaches in educational measurement that take account of patterns in time, high resolution paths of action, and clusters of virtual performance artifacts. The new approaches, which depart from traditional statistical analyses, include data mining, machine learning, and symbolic regression. This article briefly describes the context, methods and broad findings from two game-based analyses and describes key explanatory constructs use to make claims about the users, as well as the implications for design of digital game-based learning and assessment applications.
“Serious Games” is a unique industry that is concerned with the training/learning performance assessment of its clients. It is one of three digital technology industries (along with digital games, and online learning) that are rapidly advancing into the arena of analytics. The analytics from these industries all came from the tracing of user-generated data as they interacted with the systems, but differed from one another in the primary purposes for such analytics. For example, the purpose of game analytics is to support the growth of digital (entertainment) games, while that of learning analytics is to support the online learning industries. Although some game and learning analytics can indeed be used in serious games, they lack specific metrics and methods that outline the effectiveness of serious games—an important feature to stakeholders. Serious Games Analytics need to provide (actionable) insights that are of values to the stakeholders—specific strategies/policies to improve the serious games, and to (re)train or remediate play-learners for performance improvement. Since the performance metrics from one industry are unlikely to transfer well into another industry, those that are optimal for use in the Serious Games industry must be properly identified as Serious Games Analytics—to properly measure, assess, and improve performance with serious games.
Self-paced reading and eye-tracking can be used to measure microlevel student engagement during science instruction. These methods imply a definition of engagement as the quantity and quality of mental resources directed at an object and the emotions and behaviors entailed. This definition is theoretically supported by models of reading comprehension and visualization comprehension. The use of eye-movement data is based on a number of assumptions, including the assumption that people look longer at words and images because they are thinking about those objects more. Self-paced reading and eye-tracking have strengths such as precision and detail as well as limitations including difficulty of interpretation. Carefully controlled research designs and triangulation of multiple methods are suggested as possible ways to address the limitations and take advantage of the strengths. Two examples drawn from the refutational text literature are described.
Constructionism can be a powerful framework for teaching
complex content to novices. At the core of constructionism is the suggestion that by enabling learners to build creative artifacts that require complex content to function, those learners will have opportunities to learn this content in contextualized, personally meaningful ways. In this paper, we investigate the relevance of a set of approaches broadly called “educational data mining” or “learning analytics” (henceforth, EDM) to help provide a basis for quantitative research on constructionist learning which does not abandon the richness seen as essential by many researchers in that paradigm. We suggest that EDM may have the potential to support research that is meaningful and useful both to researchers working actively in the constructionist tradition but also to wider communities. Finally, we explore potential collaborations between researchers in the EDM and constructionist traditions; such collaborations have the potential to enhance the ability of constructionist researchers to make rich inferences about learning and learners, while providing EDM researchers with many interesting new research questions and challenges.
Digital game-based learning, especially massively multiplayer online games, has been touted for its potential to promote student motivation and complex problem-solving competency development. However, current evidence is limited to anecdotal studies. The purpose of this empirical investigation is to examine the complex interplay between learners' motivation, engagement, and complex problem-solving outcomes during game-based learning. A theoretical model is offered that explicates the dynamic interrelationships among learners' problem representation, motivation (i.e., interest, competence, autonomy, relatedness, self-determination, and self-efficacy), and engagement. Findings of this study suggest that learners' motivation determine their engagement during gameplay, which in turn determines their development of complex problem-solving competencies. Findings also suggest that learner's motivation, engagement, and problem-solving performance are greatly impacted by the nature and the design of game tasks. The implications of this study are discussed in detail for designing effective game-based learning environments to facilitate learner engagement and complex problem-solving competencies. © International Forum of Educational Technology & Society (IFETS).
Digital badges provide new affordances for online educational activities and experiences. When used with points and leaderboards, a badge can become a gamification element allowing learners to compete with themselves or others, and to know how close they are to accomplishing a goal and acquiring its accompanying reputation. In this role, badges motivate continued engagement, which increases time on task and supports skill acquisition through performance. Learning outcomes signified by badges can also be displayed in an e-portfolio or on web sites and are highly transportable to social media sites. In this role they summarize achievement and signal accomplishment. With these characteristics, digital badges have the potential to become an alternative credentialing system, providing visible recognition in digital symbols that link directly via metadata to validating evidence of educational achievements in public displays. This paper will trace the brief history of digital badges, define what they are, give examples of their use, and discuss their educational affordances.
This article considers the developing field of learning analytics and argues that to move from small-scale practice to broad scale applicability, there is a need to establish a contextual framework that helps teachers interpret the information that analytics provides. The article presents learning design as a form of documentation of pedagogical intent that can provide the context for making sense of diverse sets of analytic data. We investigate one example of learning design to explore how broad categories of analytics—which we call checkpoint and process analytics—can inform the interpretation of outcomes from a learning design and facilitate pedagogical action.
The important but little understood problem that motivated this study was the lack of research on valid assessment methods to determine progress in higher-order learning in situations involving complex and ill-structured problems. Without a valid assessment method, little progress can occur in instructional design research with regard to designing effective learning environments to facilitate acquisition of expertise in complex, ill-structured knowledge domains. In this paper, we first present a method based on causal representations for assessing progress of learning in complex, ill-structured problem solving and discuss its theoretical framework. Then, we present an experimental study investigating its validity against adapted protocol analysis. This study explored the impact of a massively multiplayer online educational game, which was designed to support an interdisciplinary STEM education on ninth-grade students’ complex, ill-structured problem solving skill acquisition. We identify conceptual similarities and differences between the two methods, present our comparative study and its results, and then discuss implications for diagnostics and applications. We conclude by determining how the two approaches could be used in conjunction for further research on complex and ill-structured problem solving.
Student feedback is a contentious and confusing issue throughout higher education institutions. This paper develops and analyses two models of feedback: the first is based on the origins of the term in the disciplines of engineering and biology. It positions teachers as the drivers of feedback. The second draws on ideas of sustainable assessment. This positions learners as having a key role in driving learning, and thus generating and soliciting their own feedback. It suggests that the second model equips students beyond the immediate task and does not lead to false expectations that courses cannot deliver. It identifies the importance of curriculum design in creating opportunities for students to develop the capabilities to operate as judges of their own learning.
Over the past decade, state legislatures have experienced increasing pressure to hold higher education accountable for student learning. This pressure stems from several sources, such as increasing costs and decreasing graduation rates. To explore the feasibility of one approach to measuring student learning that emphasizes program improvement, we administered several open-ended tests to 1365 students from 14 diverse colleges. The strong correspondence between hand and computer assigned scores indicates the tests can be administered and graded cost effectively on a large scale. The scores were highly reliable, especially when the college is the unit of analysis; they were sensitive to years in college; and they correlated highly with college GPAs. We also found evidence of value added in that scores were significantly higher at some schools than at others after controlling on the schools mean SAT score. Finally, the students said the tasks were interesting and engaging.
Good computer and video games like System Shock 2, Deus Ex, Pikmin, Rise of Nations, Neverwinter Nights, and Xenosaga: Episode 1 are learning machines. They get themselves learned and learned well, so that they get played long and hard by a great many people. This is how they and their designers survive and perpetuate themselves. If a game cannot be learned and even mastered at a certain level, it won't get played by enough people, and the company that makes it will go broke. Good learning in games is a capitalist-driven Darwinian process of selection of the fittest. Of course, game designers could have solved their learning problems by making games shorter and easier, by dumbing them down, so to speak. But most gamers don't want short and easy games. Thus, designers face and largely solve an intriguing educational dilemma, one also faced by schools and workplaces: how to get people, often young people, to learn and master something that is long and challenging--and enjoy it, to boot.
Complex problem solving takes place for reducing the barrier between a given start state and an intended goal state with the help of cognitive activities and behavior. Start state, intended goal state, and barriers prove complexity, change dynamically over time, and can be partially intransparent. In contrast to solving simple problems, with complex problems at the beginning of a problem solution the exact features of the start state, of the intended goal state, and of the barriers are unknown. Complex problem solving expects the efficient interaction between the problem-solving person and situational conditions that depend on the task. It demands the use of cognitive, emotional, and social resources as well as knowledge (see Frensch and Funke 1995).
The main objective of higher education institutions is to provide quality
education to its students. One way to achieve highest level of quality in
higher education system is by discovering knowledge for prediction regarding
enrolment of students in a particular course, alienation of traditional
classroom teaching model, detection of unfair means used in online examination,
detection of abnormal values in the result sheets of the students, prediction
about students' performance and so on. The knowledge is hidden among the
educational data set and it is extractable through data mining techniques.
Present paper is designed to justify the capabilities of data mining techniques
in context of higher education by offering a data mining model for higher
education system in the university. In this research, the classification task
is used to evaluate student's performance and as there are many approaches that
are used for data classification, the decision tree method is used here. By
this task we extract knowledge that describes students' performance in end
semester examination. It helps earlier in identifying the dropouts and students
who need special attention and allow the teacher to provide appropriate
advising/counseling. Keywords-Educational Data Mining (EDM); Classification;
Knowledge Discovery in Database (KDD); ID3 Algorithm.
The focus of this chapter is on designing engaging educational games for cognitive, motivational, and emotional benefits. The concept of engagement is defined and its relationship with motivation and cognition are discussed. Design issues with many educational games are examined in terms of factors influencing sustained motivation and engagement. A theoretical framework to design engaging digital games is presented, including three dimensions of engagement (i.e., behavioral, cognitive, and emotional). Later, the chapter considers how to harness the appealing power of engaging games for designing engaging educational games. Various motivational features of game design and learner experiences are considered. In conclusion, the chapter also discusses various methods to assess engagement in order to inform the design of educational games that motivate learners.
This second volume of papers from the ATC21STM project deals with the development of an assessment and teaching system of 21st century skills. Readers are guided through a detailed description of the methods used in this process. The first volume was published by Springer in 2012 ( Griffin, P., McGaw, B. & Care, E., Eds., Assessment and Teaching of 21st Century Skills, Dordrecht: Springer). The major elements of this new volume are the identification and description of two 21st century skills that are amenable to teaching and learning: collaborative problem solving, and learning in digital networks. Features of the skills that need to be mirrored in their assessment are identified so that they can be reflected in assessment tasks. The tasks are formulated so that reporting of student performance can guide implementation in the classroom for use in teaching and learning. How simple tasks can act as platforms for development of 21st century skills is demonstrated, with the concurrent technical infrastructure required for its support. How countries with different languages and cultures participated and contributed to the development process is described. The psychometric qualities of the online tasks developed are reported, in the context of the robustness of the automated scoring processes. Finally, technical and educational issues to be resolved in global projects of this nature are outlined.
When components of authentic learning are enabled by technology and the event-level interactions of learners are recorded as a historical stream of items, a voluminous and varied data record of the performance in the scenario rapidly accumulates into a transcript. This transcript, the context in which it was created, and based on it, the purposes, intentions, and practical utilities of making interpretations and inferences about what someone knows and can do is key to analysing performance in authentic digital scenarios. However, an effective assessment system not only provides a signifier of what people know via a classification and token such as a grade or badge, but also provides evidence of actions, artefacts, and processes, how knowledge was formed over time, and how well the person is able to apply the knowledge in specific situations.
This study examined the relationship between computer self-efficacy and learning performance and investigated learning engagement as a mediator of this relationship. The theoretical background is a combination of the conservation of resources (COR) theory and a theoretically extended job demands–resources model (JD-R model) proposed by Crawford, LePine, and Rich (2010). A daily diary design was carried out with 121 late-middle- and old-aged job seekers attending 10 computer cram schools. Participants completed a baseline questionnaire, seven daily diary questionnaires, and seven daily end-of-class computer skills examinations over the course of 1 week (N = 121∗7 = 847 occasions). The results of multi-level analyses showed that 1) computer self-efficacy is positively related to learning performance, 2) computer self-efficacy is positively related to learning engagement, 3) learning engagement is positively related to learning performance, and 4) learning engagement fully mediates the relationship between computer self-efficacy and learning performance. The theoretical contributions, research limitations, implications for future research, and practical implications of this study are discussed.
Higher education institutions and involved stakeholders can derive multiple benefits from learning analytics by using different data analytics strategies to produce summative, real-time, and predictive insights and recommendations. However, are institutions and academic as well as administrative staff prepared for learning analytics? A learning analytics benefits matrix was used for this study to investigate the current capabilities of learning analytics at higher education institutions, explore the importance of data sources for a valid learning analytics framework, and gain an understanding of how important insights from learning analytics are perceived. The findings reveal that there is a lack of staff and technology available for learning analytics projects. We conclude that it will be necessary to conduct more empirical research on the validity of learning analytics frameworks and on expected benefits for learning and instruction to confirm the high hopes this promising emerging technology raises.
This emerging technology report reviews automated knowledge visualization and assessment (AKOVIA) which is a web-based tool for the analysis of natural language and graphical knowledge representations. The open architecture of AKOVIA enables a large variety of research and practical applications. It integrates a unique language-oriented methodology based on heuristics and generates seven quantitative similarity measures on the fly. AKOVIA has been successfully tested for reliability and validity. Given the limited availability of reliable and valid automated assessment methodologies, AKOVIA has the potential to be integrated into web-based applications and thus opening up numerous research as well as practical applications.
This book provides a comprehensive, up-to-date look at problem solving research and practice over the last fifteen years. The first chapter describes differences in types of problems, individual differences among problem-solvers, as well as the domain and context within which a problem is being solved. Part one describes six kinds of problems and the methods required to solve them. Part two goes beyond traditional discussions of case design and introduces six different purposes or functions of cases, the building blocks of problem-solving learning environments. It also describes methods for constructing cases to support problem solving. Part three introduces a number of cognitive skills required for studying cases and solving problems. Finally, Part four describes several methods for assessing problem solving. Key features includes: Teaching Focus - The book is not merely a review of research. It also provides specific research-based advice on how to design problem-solving learning environments. Illustrative Cases - A rich array of cases illustrates how to build problem-solving learning environments. Part two introduces six different functions of cases and also describes the parameters of a case. Chapter Integration - Key theories and concepts are addressed across chapters and links to other chapters are made explicit. The idea is to show how different kinds of problems, cases, skills, and assessments are integrated. Author expertise - A prolific researcher and writer, the author has been researching and publishing books and articles on learning to solve problems for the past fifteen years. This book is appropriate for advanced courses in instructional design and technology, science education, applied cognitive psychology, thinking and reasoning, and educational psychology. Instructional designers, especially those involved in designing problem-based learning, as well as curriculum designers who seek new ways of structuring curriculum will find it an invaluable reference tool.
One of the challenges with research on student engagement is the large variation in the measurement of this construct, which has made it challenging to compare findings across studies. This chapter contributes to our understanding of the measurement of student in engagement in three ways. First, we describe strengths and limitations of different methods for assessing student engagement (i.e., self-report measures, experience sampling techniques, teacher ratings, interviews, and observations). Second, we compare and contrast 11 self-report survey measures of student engagement that have been used in prior research. Across these 11 measures, we describe what is measured (scale name and items), use of measure, samples, and the extent of reliability and validity information available on each measure. Finally, we outline limitations with current approaches to measurement and promising future directions.
Models of both self-regulated learning and student engagement have been used to help understand why some students are successful in school while others are not. The goal of this chapter is to provide greater insight into the relations between these two theoretical frameworks. The first section presents a basic model of self-regulated learning, outlining the primary phases and areas involved in that process. The next section discusses key similarities and differences between aspects of self-regulated learning and features of student engagement, drawing on both theoretical suggestions and empirical research. The final section offers ideas and avenues for additional research that would serve to better link self-regulated learning and student engagement.
Digital game-based learning, especially massively multiplayer online games, has been touted for its potential to promote student motivation and complex problem-solving competency development. However, current evidence is limited to anecdotal studies. The purpose of this empirical investigation is to examine the complex interplay between learners’ motivation, engagement, and complex problem-solving outcomes during game-based learning. A theoretical model is offered that explicates the dynamic interrelationships among learners’ problem representation, motivation (i.e., interest, competence, autonomy, relatedness, self-determination, and self-efficacy), and engagement. Findings of this study suggest that learners’ motivation determine their engagement during gameplay, which in turn determines their development of complex problem-solving competencies. Findings also suggest that learner’s motivation, engagement, and problem-solving performance are greatly impacted by the nature and the design of game tasks. The implications of this study are discussed in detail for designing effective game-based learning environments to facilitate learner engagement and complex problem-solving competencies.
Studies were designed to determine the effectiveness of challenge-based instruction (CBI) versus traditional lecture-based instruction. Comparisons were made over a three-year period between student performance on knowledge-based questions in courses taught with taxonomy-based and challenge-based approaches to instruction. When performance on all questions was compared, CBI classes scored significantly better than control classes on 26 percent of the questions, while control classes outperformed CBI classes on eight percent of the questions, but there was no significant difference in overall performance. However, students in CBI classes performed significantly better than students in control classes on the more difficult questions (35 percent versus four percent). We attribute these differences to additional opportunities available in CBI classrooms for learners to examine their conceptual understanding. Student surveys indicate a slight preference for the challenge-based approach. We believe that the challenge-based approach is effective and has the potential to better prepare students for the workplace and for life-long learning.
Interest in collecting and mining large sets of educational data on student background and performance to conduct research on learning and instruction has developed as an area generally referred to as learning analytics. Higher education leaders are recognizing the value of learning analytics for improving not only learning and teaching but also the entire educational arena. However, theoretical concepts and empirical evidence need to be generated within the fast evolving field of learning analytics. The purpose of the two reported cases studies is to identify alternative approaches to data analysis and to determine the validity and accuracy of a learning analytics framework and its corresponding student and learning profiles. The findings indicate that educational data for learning analytics is context specific and variables carry different meanings and can have different implications across educational institutions and area of studies. Benefits, concerns, and challenges of learning analytics are critically reflected, indicating that learning analytics frameworks need to be sensitive to idiosyncrasies of the educational institution and its stakeholders.
This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies in L2 writing that concentrate on longitudinal writing development, predicting essay quality, examining differences between L1 and L2 writers, the effects of writing topics, and the effects of writing tasks. The paper concludes with implications for the recommended replication studies in the field of L2 writing and the advantages of using NLP tools and machine learning algorithms.
In a 1992 Calvin and Hobbs cartoon (Watterson), 6-year-old Calvin asks his teacher whether he is being adequately prepared for the challenges of the 21st century. He wants to know if he will have the skills and competencies that will allow him to succeed in a tough, global economy. In response, the teacher suggests he start working harder because what he will get out of school depends on how much effort he puts into it. Calvin ponders this advice for a moment and says, "Then forget it."
The exchange between Calvin and his teacher gets right to the point about what matters to student learning and personal development. Indeed, one of the few unequivocal conclusions from How College Affects Students (Pascarella & Terenzini, 2005) is that the amount of time and energy students put forth-student engagement-is positively linked with the desired outcomes of undergraduate education. Unfortunately, Calvin's response is all too common, if not according to what students say, then by what they do or do not do.
In this paper, I summarize the role and contributions of the scholarship and institutional research about student engagement and its relevance for student development professionals and others committed to enhancing the quality of the undergraduate experience. The presentation is organized into four major sections. First, I briefly describe the evolution of the student engagement concept and explain its importance to student development. Then, I summarize findings from research studies about the relationships between student engagement and selected activities including participation in high-impact practices, employment, and some other experiences of relevant a relevance to the current generation of undergraduates. Next, I discuss some topics that warrant additional investigation to better understand how to further potential and utility of student engagement research and institutional policies and practices that the findings suggest. I close with some observations about the implications of student engagement research for student affairs professionals and others on campus committed to improving the quality of undergraduate education.
Student engagement represents the time and effort students devote to activities that are empirically linked to desired outcomes of college and what institutions do to induce students to participate in these activities (Kuh, 2001, 2003, 2009). The meaning and applications of this definition of student engagement have evolved over time to represent increasingly complex understandings of the relationships between desired outcomes of college and the amount of time and effort students invest in their studies and other educationally purposeful activities (Kuh, 2009; Wolf-Wendel, Ward, & Kinzie, 2009). For example, building on Tyler's "time on task" concept (Merwin, 1969), Pace (1980, 1984) developed the College Student Experiences Questionnaire (CSEQ) to measure "quality of effort" to identify the activities that contributed to various dimensions of student learning and personal development. His research across three decades (1960 to 1990) showed that students gained more from their studies and other aspects of the college experience when they devoted more time and energy to certain tasks that required more effort than others-studying, interacting with their peers and teachers about substantive matters, applying their learning to concrete situations and tasks in different contexts, and so forth (Pace, 1984, 1990).
Astin (1984) further fleshed out and popularized the quality of effort concept with his "theory of involvement," which highlighted the psychological and behavioral dimensions of time on task and quality of effort. His landmark longitudinal studies about the impact of college on students empirically demonstrated the links between involvement and a range of attitudinal and developmental outcomes (Astin, 1977, 1993). Astin was a major contributor to the widely cited Involvement in Learning report (National Institute of Education, 1984) which underscored the importance of involvement to student achievement and such other valued outcomes as persistence and educational attainment (Astin, 1999).
In that same decade, after an invitational conference of scholars and educators held at the Wingspread Conference Center in Wisconsin, Chickering and Gamson (1987) distilled the discussions about the features of high-quality teaching and learning settings into seven good practices in undergraduate education: (a) student-faculty contact, (b) active learning, (c) prompt feedback, (d) time on task, (e) high expectations, (f ) respect for diverse learning styles, and (g...
Although problem solving is regarded by most educators as among the most important learning outcomes, few instructional design
prescriptions are available for designing problem-solving instruction and engaging learners. This paper distinguishes between
well-structured problems and ill-structured problems. Well-structured problems are constrained problems with convergent solutions
that engage the application of a limited number of rules and principles within well-defined parameters. Ill-structured problems
possess multiple solutions, solution paths, fewer parameters which are less manipulable, and contain uncertainty about which
concepts, rules, and principles are necessary for the solution or how they are organized and which solution is best. For both
types of problems, this paper presents models for how learners solve them and models for designing instruction to support
problem-solving skill development. The model for solving well-structured problems is based on information processing theories
of learning, while the model for solving ill-structured problems relies on an emerging theory of ill-structured problem solving
and on constructivist and situated cognition approaches to learning.
This study examines (1) the extent to which student engagement is associated with experimental and traditional measures of
academic performance, (2) whether the relationships between engagement and academic performance are conditional, and (3) whether
institutions differ in terms of their ability to convert student engagement into academic performance. The sample consisted
of 1058 students at 14 four-year colleges and universities that completed several instruments during 2002. Many measures of
student engagement were linked positively with such desirable learning outcomes as critical thinking and grades, although
most of the relationships were weak in strength. The results suggest that the lowest-ability students benefit more from engagement
than classmates, first-year students and seniors convert different forms of engagement into academic achievement, and certain
institutions more effectively convert student engagement into higher performance on critical thinking tests.
Some of the empirical research papers focusing on improving instructional design from a cognitive load theory (CLT), included in the Third International Cognitive Load Theory Conference held at the Open University, Heerlen, The Netherlands, 2009, are compiled. CLT uses current knowledge about the human cognitive architecture to generate instructional techniques. Baddeley & Hitch found that this architecture consists of an effectively unlimited long-term memory (LTM), which interacts with a working memory (WM) that is very limited in both capacity. The empirical evidence of a learning process occurring over a long period of time is in the study of chess grandmasters by De Groot, 1946, 1978 and Simon & Gilmartin, 1973. Lee and Kalyuga, (2011) studied the multimedia redundancy effect in using pinyin to learn the Chinese language. Wetzels, Kester, and Van Merriënboer, (2011) used a static multimedia learning environment to teach students about the functioning of the heart.
Digital Game-Based Learning, by Marc Prensky, is a strategic and tactical guide to the newest trend in e-learning - combining content with video games and computer games to more successfully engage the under-40 "Games Generations," which now make up half of America's work force and all of its students. The book fully explores the concept of Digital Game-Based Learning, including such topics as How Learners Have Changed, Why Digital Game-Based Learning Is Effective, Simulations and Games, How Much It Costs, and How To Convince Management. With over 50 case studies and examples, it graphically illustrates how and why Digital Game-Based Learning is working for learners of all ages in all industries, functions and subjects.
The field of discourse processing has dissected many of the levels of representation that are constructed when individuals read or listen to connected discourse. These levels include the surface code, the propositional textbase, the referential situation model, the communication context, and the discourse genre. Discourse psychologists have developed models that specify how these levels are mentally represented and how they are dynamically built during comprehension. This chapter focuses on the meaning representations that are constructed when adults read written text, such as literary stories, technical expository text, and experimenter-generated "textoids." Recent psychological models have attempted to account for the identification of referents of referring expressions (e.g. which person in the text does she refer to), the connection of explicit text segments, the establishment of local and global coherence, and the encoding of knowledge-based inferences.
Foundations of digital badges and micro-credentials
- D Ifenthaler
- N Bellin-Mularski