Immersion in e-Learning

Abstract

Flow is a state of intense concentration and engagement, when a user is so immersed in her activity, that all other external influences cease. It is a well-known fact that flow is experienced in games, where we all had the 'just one more minute' request from our children. This paper analyses the notion of flow from two perspectives: the theoretical concepts and the practical reality. For the latter, game environments are compared to current elearning environments. Finally, the extracted features are mapped back to the theoretical underpinning.

Keywords

• web

CCS CONCEPTS

• Human-centred computing → Interaction paradigms → Hypertext / hypermedia; Web-based interaction; Applied computing → Education → e-learning; Information systems → Decision support systems → Data analytics;

INTRODUCTION

In e-learning, immersion is a concept based on the psychological concept of flow: learners 'are so engaged in learning, that time and fatigue disappear' [ 1,2 ]. We mostly know this experience from online and offline games. The challenge is to create e-learning offers that can lead to a similar intense involvement. This seems to be an impossible challenge for educational software, and thus represents almost a 'holy grail' for online education. This is especially relevant now, with the rising of MOOCs, such as Corsera in the US and FutureLearn in Europe, backed up strongly, in a top-down fashion, by current politicians, but which suffer greatly from extremely high dropout rates.

Flow Components

The noted psychologist Csikszentmihalyi identified 4 key components of flow: control (learner's control over the experience); attention (learner's dedication to the task at hand); intrinsic interest (motivated by the desired outcome) and curiosity (leading them forward) [ 3 ]. Specifically,

Csikszentmihalyi recommends as flow antecedents clear goals & immediate feedback, and a good challenge & skills balance. 3

IMMERSION FEATURES

An appropriate source for extracting immersion and flow-related features are games. It is easy, at first glance, to attribute the typically high level of immersion in game environments to advanced computer graphics (such as Halo 51), or 3D interactivity (such as in EVE Online2); however, this is only part of the answer. It also represents the part that is more difficult to implement, requiring large teams of dedicated programmers. In the following I identify some tangible features of current game environments that are much more straightforward to implement, but that are currently missing in current e-learning environments (even in more advanced adaptive or personalised ones [ 4,5 ]), which may trigger immersion [ 6 ]. The focus here is mainly on feedback, which is considered an essential aspect to be supported in elearning [ 7 ]. 1) Game environments, unlike TEL environments, often have multi-dimensional levels of interactivity and feedback. Thus, unlike in a learning system, where feedback often relates to scores, marks, or percentage of progress, which all reflect, in principle, the single dimension of knowledge-increase, in game environments various parameters can be tracked, and the user can progress in various ways, as defined by these parameters. 2) Next, in game environments, the feedback is frequent. At each ‘kill’ or ‘success’, for instance, the popular first-player-shooter games immediately display on-screen the experience feedback. Opposed to that, most learning environments, adaptive or not, give delayed feedback, often significantly so, only, e.g., when a whole chapter is read, or an important concept is mastered. 3) Moreover, the feedback is highly visible. Games often place their feedback in the middle of the screen, as in the previous example, with perhaps strong colours, or even graphics. In learning environments, especially in adaptive settings, a lot of discussions have centred on the benefits of high level of feedback. 4) Furthermore, in game environments, the feedback is finegrained. At each event, popular games immediately display experience feedback. A player feels at all times that she is making some progress. Opposed to that, most learning environments, adaptive or not, display quite a coarse-grained feedback. 5) Additionally, feedback is volatile. This means, feedback doesn’t linger on the screen for very long. 1 https://www.halopedia.org/Halo_5:_Guardians 2 https://www.eveonline.com/ 6) Moreover, feedback is traceable. Whilst the information can flash quickly in front of the user, a user in a game environment can usually find, for instance, information about a certain achievement, when desired. Adaptive learning environments are more concerned with keeping track of the current state (e.g., the percentage of current knowledge) instead of storing minor achievements. 7) Adaptive educational environments may often track the distance to achievement, instead of challenges conquered. I.e., as in the above example, the percentage of knowledge as compared to the desired state of knowledge may be displayed. Instead, game environments display achievements, experience levels, ranking in a leader board etc. 8) Furthermore, interactivity with other players is often used in multi-player games as an extra dimension for exploration. Competition and collaboration are encouraged via reward systems. Whilst interactivity and collaboration have been explored in previous work on TEL environments (e.g., the ALS EU project3 led by Warwick), its effect on the learning immersion is not yet fully explored and exploited. 9)VAccess to information (such as feedback) can be obtained in multiple, redundant ways in game environments. In such cases, redundancy is no undesirable feature, but the contrary: users can get to the information in whichever way they are more comfortable with. Contrary to this, educational environments rarely introduce redundancy in their paths. 4

DISCUSSION

Analysing how the features above map onto the theory provides the reassurance that these are indeed features which could be scaffolding flow and ultimately immersion in a learning environment, as follows. 1a) multi-dimensional levels of interactivity: in terms of flow antecedents, it maps onto 'challenge & skills balance'. In terms of the key components, it maps best onto 'control', although it allows also the learner to follow their 'intrinsic interest' and nurture 'curiosity', which could enhance 'attention'. 1b) multi-dimensional feedback: in terms of flow antecedents, it maps well onto the 'immediate feedback'. In terms of the key components, it maps onto 'attention' (as it keeps a learner's attention by the various type of feedback), gives a feeling of 'control', which could increase 'intrinsic interest' and 'curiosity'. 2) frequent feedback: directly maps to 'immediate feedback', and is generally related to 1b) above. 3) highly visible feedback: spurs 'attention', potentially increasing 'curiosity' and 'intrinsic interest', as well as giving a greater feeling of 'control'. 4) fine-grained feedback: allows clearly for 'control' - the learner is in charge and aware of most aspects of her learning. Importantly, in opposition to previous research, which was concerned about learning overhead [ 8 ], in fact, modern learners are well equipped to deal with a multitude of inputs online, without losing focus [ 9, 10 ]. 5) volatile feedback: this feature supports 'immediate feedback' by still allowing for learner's 'attention' to the task at hand (instead of interrupting with interaction demands). 6) traceable feedback: this features supports 'immediate feedback' by helping, when 'interest' and 'attention' are caught, to easily be in 'control' and find the required information. 7) challenges conquered, not distance to achievement: this deals with the 'challenge & skills balance', in that, even harder problems, are presented in a manner in which they can be perceived as simpler and approachable, where skills and challenge are matched. 8) social aspects: interestingly, instead of detracting from a learner's attention', social interaction can actually help to improve the 'challenge & skills' balance, and lower achievement thresholds, by involving help from peers, tutors, etc. Such aspects, if well-implemented, can increase the level of perceived learner 'control', and thus increase the motivation and 'interest'. 9) redundant access to information: a less intuitive outcome, the availability of multiple paths to the same piece of information (be it feedback, or learning content, or social interaction) helps towards learner 'control', adjusting to the learner's mental model of the information organisation, and lowers the challenge from a system perspective, thus achieving a better 'challenge & skills balance'. 5

IMMERSION FRAMEWORK

CONCLUSIONS

In summary, this paper has proposed a new method of adding flow-related features to an e-learning environment, by extracting relevant flow-inducing features from games environments, which reflect the quintessence of user immersion, and creating an initial framework for researchers. However, instead of being stuck at a superficial level - such as assuming that e-learning has to be delivered via games, in order to induce flow, or that it has to include high resolution 2D or even 3D graphics, the focus was on the more often overlooked aspects of games. Still, the results are not comprehensive. Results mainly centre on 'feedback', and, for instance, 'goals' have not been studied at all. This study thus encourages researchers to further explore such features, to better reflect the intrinsic requirements of flow theory, and achieve the immersive environments for the learners of the future. Recently, a new generation of e-learning tools are incorporating such elements that are aimed at flow [ 11-20 ]. However, these researches are few and far between, and a broader effort of the research community in general is needed, to get closer to this 'holy grail' of online education.

References

[1] Mihaly Csikszentmihalyi . 1991 . Flow: The Psychology of Optimal Experience, HarperCollins , ISBN 0-0609-2043-2.

[2] Mihaly Csikszentmihalyi . 1997 . Finding Flow: The Psychology Of Engagement With Everyday Life, books .google. com [last accessed 09 /06/2017]

[3] Pappas , C. 2016 . Applying the flow theory in online training , eLearning Industry, https://elearningindustry.com /applying-the-flow-theory-in-onlinetraining [last accessed 09 /06/2017]

[4] O 'Donnell , E. , Lawless , S. , Sharp , M. & V.Wade. ( 2015 ). A Review of Personalised E-Learning: Towards Supporting Learner Diversity . International Journal of Distance Education Technologies , pp. 22 - 47 . DOI: 10 .4018/ijdet.2015010102

[5] Ashman , H. , Brailsford , T. , Cristea , A. I. , Sheng , Q. Z. , Stewart , C. , Toms E. and Wade , V. 2014 . The Ethical and Social Implications of Personalisation Technologies for e-Learning International Journal of Information Management (IJIM), Special Issue on IS Ethics: Past, Present and Future at Information & Management, Elsevier, 51 ( 6 ), September 2014 , p. 819 - 832 , DOI: 10.1016/j.im. 2014 . 04 .003

[6] Konradt , U. , Filip , R. , Hoffman , S. 2003 . Flow experience and positive affect during hypermedia learning , British Journal of Educational Technology, DOI: 10 .1111/ 1467 - 8535 . 00329

[7] Aswek , S. ( 2000 ) Feedback for learning , Routledge Falmer , Taylor & Francis Group.

[8] Chen , F. et al. 2016 . The State-of-the- Art . Chapter, Robust Multimodal Cognitive Load Measurement , Part of the Human-Computer Interaction Series , 13 - 32 , Springer.

[9] Zumbach , J. , Mohraz , M. 2008 . Cogitive load in hypermedia reading comprehension: Influence of text type and linearity , Computers in Human Behavior 24 , 875 - 887 , Elsevier.

[10] Zunbach , J. 2006 . Cognitive Overhead in Hypertext Learning Re-examined: Overcoming the Myths , J. of Educational Multimedia and Hypermedia , 15 ( 4 ), 411 - 423 .

[11] Liu S-H. , Liao H-L. , Pratt J.A. 2009 . Impact of media richness and flow on elearning technology acceptance , Computers & Education , Elsevier, 52 , 599 - 607 .

[12] Lee , M-C. 2010 . Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model , Computers & Education 54 , 506 - 516 .

[13] Shi , L. , Cristea , A. I. , Awan, MSK., Hendrix , M. , Stewart C. 2013 . Towards understanding learning behavior patterns in social adaptive personalized elearning systems , AMCIS , 1 - 10 , Chicago, Illinois, USA, August 15 - 17.

[14] Shernoff , D. J. . et al. 2014 . Measuring Flow in Educational Games and Gamified Learning Environments , EdMedia, Tampere, Finland, June 23-26.

[15] Shi , L. , Cristea , A. I. , Hadzidedic , S. , Dervishalidovic , N. 2014 . Contextual Gamification of Social Interaction -Towards Increasing Motivation in Social ELearning , In: 13th International Conference on Web - based Learning (ICWL2014) , Tallinn, Estonia, 14 - 17 .

[16] Shi , L , Gkotsis, G. , Stepanyan , K. , Al Qudah , D. , Cristea, AI . 2014 . Social personalized adaptive e-learning environment: Topolor-implementation and evaluation . Int.Conference on Artificial Intelligence in Education , 708 - 711 .

[17] Cristea , A. I. and Shi , L. 2016 . Motivational gamification strategies rooted in self-determination theory for social adaptive E-Learning . In: Intelligent Tutoring Systems (ITS) 2016 , Zagreb, Croatia., 6 - 10 Jun 2016 .

[18] Shi , L. and Cristea , A. I. 2016 . Simplifying is not always best : learners thrive when using multifaceted open social learner models . IEEE MultiMedia , 23 ( 1 ), 36 - 47 .

[19] Rodriguez-Ardura , I. and Meseguer-Artola , A. 2016 . E-learning continuance: The impact of interactivity and the mediating role of imagery, presence and flow , Information & Management Journal, Elsevier 53 , 504 - 516 .

[20] Rodriguez-Ardura , I. , Meeguer-Artola , A. 2016 . Flow in e-learning: What drives it and why it matters , British Journal of Educational Technology, doi: 10 .111/bjet.12480.