Leveraging the pandemic to build a community of networked, engaged and curious learners – our future leaders

The COVID-19 pandemic has undoubtedly presented a multitude of challenges to the way education is delivered; its wide-reaching multidisciplinary impact has also presented a unique opportunity as a focus for real-world authentic learning. For some time now, technology has enabled interaction at a global scale, allowing students to connect with teachers and industry experts around the world. This paper reports on the innovative design of an intra-curricular program utilising COVID-19 as a focus for online, real-world connected learning, delivered to business students at a large Australian university during the pandemic lockdown. Implemented as an online intra-curricular initiative, ‘Leading in a Post-COVID World’ encouraged student engagement with the challenges of leadership to address issues on a personal, local, and global scale. Using a community of inquiry (CoI) lens we explore key features of the program and find that a CoI approach combined with principles of real-world learning and authentic experiences encourages student participation and engagement in this intra-curricular space.

The Validation of Digital Analysis Tool-Assisted Real-World Inquiry (Digita-RI) as a Modification of the Inquiry-Based Learning Model in the Digital Age

Inquiry-based learning has been tested to improve conceptual understanding, reduce misconceptions, and provide students with experiences in scientific work. However, in its implementation, inquiry-based learning is often faced with scientific facts from the real world with data which hard to analyze using traditional methods. Therefore, a breakthrough is needed to overcome the weaknesses of inquiry-based learning by integrating digital analysis tools and the concept of real-world learning. This integration produces a new learning model, the Digital Analysis Tool-Assisted Real-World Inquiry (Digita-RI). This study aims to test the feasibility and practicality of the Digita-RI learning model. This Research and Development (R&D) use the steps proposed by Barg and Gall. The feasibility test of the Digita-RI model was carried out through the Focus Group Discussion (FGD) method and the assessment of the Digita-RI model book involving seven experts. The practicality test was carried out through the Think Aloud Protocol (TAP), and the assessment of the Digita-RI model guidebook involved five practitioner lecturers and six students. The results of expert, practitioner, and user assessments were analyzed using the Aiken coefficient (Aiken’s V). The results showed that Digita-RI is a feasible and practical learning model. Therefore, it can be concluded that Digita-RI has the feasibility and practicality to be used in science learning in the classroom.

Learning of a Basketball Free Throw With a Flexible Link Robot

Object throwing is an efficient approach for overcoming the kinematic workspace limitations of robots in placement scenarios. Throwing of objects with rigid link robots has been widely studied in literature. Although using robots with spring-like flexible links can significantly increase the throwing distance, existing contributions are very rare. Therefore, we propose an efficient iterative learning control throwing algorithm and apply it to a flexible link robot. A simple rigid link throwing model is used to generate the motor motion. Errors caused by this simplification are corrected by a flexible link throwing model based on the finite element method. As representative scenario a basketball free throw is selected which requires high throwing accuracy. Here, we demonstrate that the controller can be efficiently pre-learned in simulations to reduce real-world training time. Experiments then validate that our learning control method achieves the required free throw accuracy within very few real-world learning iterations.

General knowledge and detailed memory benefit from different training sequences

Real-world learning contexts sometimes require the use of general knowledge, whereas others depend on recalling detailed information about individual events. By combining category learning with trial-unique source information, we examined how different learning sequences (blocked vs. interleaved) impact the acquisition of generalized (category-level) and detailed (exemplar-specific) knowledge. Participants were trained to identify paintings by different artists, half of which were studied in a sequence blocked by artist and the remainder interleaved between artists. Participants were tested on general knowledge (category induction) and detailed memory (source recall), both immediately after learning and a 1-week delay. We found that interleaved learning improved general knowledge, but blocked learning improved detailed memory. Furthermore, we found that general knowledge remained stable whereas detailed memory performance declined after a delay. Our results indicate that optimal training conditions differ based on the goals of learning such as enhancing general knowledge or improving memory of individual event details.

Developing a Scaffolded, Structured Approach to Road Safety Education in Schools

Scaffolding is a well-established approach to education to maximise student learning outcomes. The premise of this paper is that there is a need for formal, scaffolded road safety education (RSE) which can be delivered in schools in Australasia. This paper supports the education system as being expert in matching human growth and developing scaffolds on which to build learning stages and presents arguments to show that an RSE scaffold can and should be drawn up. Schools can provide a structured in-class and real world learning experiences within that scaffold, which, with suitable communication, can be backed up by the home and the broader community. An integrated RSE scaffold across primary through secondary schooling is currently lacking in Australasia, but could be readily integrated in current school curricula. This paper calls for such developments and welcomes further debate and implementation of next steps to achieve this.

Instructor-learner neural synchronization during elaborated feedback predicts learning transfer

The provision of feedback with complex information beyond the correct answer, i.e., elaborated feedback, can powerfully shape learning outcomes such as transfer. However, an understanding of neurocognitive mechanisms that support elaborated feedback during instructor-learner interactions remains elusive. Here, a two-person interactive design is used during simultaneous recording of functional near-infrared spectroscopy (fNIRS) signals from adult instructor-learner dyads. Instructors either provided elaborated feedback (i.e., correct answer and an example) or simple feedback (i.e., correct answer only) to learners during a concept learning task. Our results showed that elaborated feedback produced comparable levels of retention to simple feedback, however, transfer was significantly enhanced by elaboration. We also noted significant instructor-learner neural synchronization in frontoparietal regions during the provision of elaborated feedback, especially when examples were provided. Further, interpersonal neural synchronization in the parietal cortex successfully predicted the transfer of knowledge to novel contexts. This prediction was retained for both learner-delayed and learner-preceding neural synchronization, supporting the interpretation that deeper-level representations of knowledge, such as abstract structure and personal interpretation, may promote the transfer of learning. These findings point toward interpersonal neural synchronization as a key neurocognitive mechanism that supports learning transfer effects, and may have important implications for real-world learning and pedagogical efficacy.Educational Impact and Implications StatementFeedback provides the information regarding the gap between what is achieved and what is aimed to be achieved, and thus plays a critical role in any learning processes. In real-world settings, feedback is oftentimes provided and received during social interactions, and contains complex information beyond the correct answer, that is elaborated feedback. This study sought to investigate neurocognitive mechanisms that support elaborated feedback during instructor-learner interactions using fNIRS hyperscanning. It was revealed that providing learners with elaborated feedback enhanced the transfer of knowledge to novel contexts relative to simple feedback. Instructor-learner neural synchronization was detected in frontoparietal regions during the provision of elaborated feedback, especially for examples. Parietal instructor-learner neural synchronization predicted the transfer. This study provides a novel lens, i.e., interpersonal neural synchronization, for people to understand more about how elaborated feedback takes effects on learning transfer, and may have critical implications for real-world learning and pedagogical efficacy.

Mobile Technology

The digital transformation of society is reaching a state of maturity, which provides people with new and exciting possibilities (NESTA, 2019) and implies a preponderant change in terms of inclusive collaboration, human-centred global economy and governance. Conjointly, ‘knowledge is the impetuous for communication’ (Carayannis & Clark, 2011, p. 203) with respect to foster ‘social capital’ and to thrive ‘cultural knowledge’ (Levallet & Chan, 2019, p. 182). Within this context, mobile technology, thanks to its affordance (Ahonen, 2011; Volkoff & Strong, 2013) and its contextuality (Cochrane et al., 2016), can enable creativity which supports the Cognitive Process Dimension (Anderson et al., 2001). Scilicet, mobile devices become the interface between people and processes (Morel et al., 2018; Dampérat et al., 2019) in relation to innovative practices (Makri et al., 2017) and real-world learning (Saleh et al., 2019) in formal and informal contexts. Moreover, it can enhance the developing of ideas inner/outer an organisation, or a classroom (Hall et al., 2020), and the serendipity flow of learning experiences (Makri et al., 2015). To a certain extent, mobile technology can bolster ‘collective knowledge’ (Pont, 2013; Levallet & Chan, 2019) by enabling quick decision-making and by connecting with a glocal network (Antonczak, 2021). From a transdisciplinary approach, amidst learning sciences (Sommerhoff et al., 2018), management and organisational research, this presentation canvasses mobile technology (Jones & Marsden, 2006; Ahonen, 2011) as being a key apparatus and interface for collaborative innovation (Demil & Lecocq, 2012; Suire et al., 2018), which allows organisations to develop their ‘information ecology’ (Nardi, 1999) through a dynamic sense of what is inside and what is outside their boundaries. Said differently, it deciphers how mobile technology can enable exchange information and co-creative practices beyond formal structures and systems across industries and/or academia. To start, the presentation quickly outlines some key concepts from an inter-disciplinary literature reviews (Baumeister & Leary, 1997), including collaboration, creativity, knowledge dynamics such as knowledge creation and/or conversion (Sawyer, 2008) as well as ‘knowledge retention and/or knowledge loss’ (Levallet & Chan, 2019). Next, it epitomises a few technological enabling conditions (Makri, 2017; Levallet & Chan, 2018; Cheng et al., 2019) such as autonomy, diversity, interactivity, contextuality through mobile social media and mobile-first applications (Apps) in relation to collaboration and learning practices beyond the limits of a physical environment. Then, it introduces the methodological and qualitative approach used for the analysis and findings, as well as the interpretations of practices in Education and Business. Finally, this presentation concludes with some features about how mobile technology practices support collaborative and innovative learning environments, the co-creation of new frameworks, and it suggests further avenues for supplementary research.

How to train your robot with deep reinforcement learning: lessons we have learned

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.