Collaborative Learning Scenarios Research Articles

Exploring the Role of TPACK in Promoting Inquiry-based Learning in 21

The realm of science education is grappling with the growing demand to equip learners with the scientific reasoning and technological capabilities vital for the 21st century. In light of this, the present study delves into how Technological Pedagogical Content Knowledge (TPACK) can enhance inquiry-driven approaches in modern-day science education. Using a literature review as its methodological approach, this research ascertains that TPACK is pivotal in making inquiry-based learning more accessible in science pedagogy. Specifically, educators who possess a profound grasp of TPACK can seamlessly weave technology into science lessons, tailor their teaching methods to cater to student requirements, and combine scientific factual knowledge with analytical reasoning abilities. As it pertains to applying an inquiry-driven curriculum, TPACK empowers educators to design engaging, collaborative, and experiential learning scenarios within the ambit of scientific exploration. Consequently, this study sheds light on the instrumental role of TPACK in science education of the 21st century, advocating for a more dynamic and learner-focused approach. This research underscores the need for specialized training and continual professional enhancement with a spotlight on cultivating TPACK skills among science educators. Furthermore, the insights derived from this study can pave the way for avant-garde curriculum frameworks and pedagogical structures, further championing inquiry-oriented instruction in contemporary science education.
 Keywords: TPACK, science education, inquiry based learning, 21st century

A hybrid model for energy-efficient Green Internet of Things enabled intelligent transportation systems using federated learning

In the rapidly evolving Internet of Things domain, managing voluminous data streams while ensuring energy efficiency remains a cardinal challenge. Our research introduces a hybrid model designed specifically for an IoT system. This paper sheds light on our unique framework that emphasizes efficient communication and prioritizes energy conservation. The innovative model architecture is lightweight, fostering compatibility with resource-constrained devices and paving the way for personalized federated averaging during training. A crucial highlight of our methodology includes local training using lightweight optimization instead of traditional data transmission, reducing energy overheads considerably. Furthermore, we propose a novel approach for model aggregation using personalized energy-aware averaging. This process iteratively refines a global model by aggregating received updates, drastically reducing the data transfer compared to conventional methods. Lastly, we integrate an energy-aware updates management system, continually monitoring device energy metrics and making adaptive data transmission and model update decisions. The model ensures optimal participation in global updates by consistently monitoring devices’ energy metrics and setting adaptive thresholds. This study leverages the richly annotated Udacity Self-Driving Car Dataset provided by Roboflow to evaluate a novel federated learning model to optimize energy consumption for IoT systems. Our experiments simulate real-world collaborative learning scenarios using the TensorFlow Federated (TFF). Our focus on energy consumption evaluation revealed that our proposed model offers significant energy savings when analyzed based on data communication round time and individual node training duration. A comparative analysis between the proposed and traditional models uncovers substantial improvements in energy efficiency. In our experiments, the proposed approach demonstrated a commendable accuracy of 93.27%. Notably, the local communication time was streamlined to 1.21 s, while the global communication was efficiently clocked at 4.76 s. When compared to traditional methods, these results not only underscore the effectiveness and efficiency of our methodology in the context of IoT systems but also highlight its superior performance.

A Multi-Shuffler Framework to Establish Mutual Confidence for Secure Federated Learning

Albeit the popularity of federated learning (FL), recently emerging model-inversion and poisoning attacks arouse extensive concerns towards privacy or model integrity, which catalyzes the developments of secure federated learning (SFL) methods. Nonetheless, the collisions between its privacy and integrity, two equally crucial elements in collaborative learning scenarios, are relatively underexplored. Individuals' wish to “hide in the crowd” for privacy frequently clashes with aggregator' need to resist abnormal participants for integrity (i.e., the incompatibility between Byzantine robustness and differential privacy). The dilemma prompts researchers to reflect on how to build mutual confidence between individuals and aggregators. Against the backdrop, this paper proposes a multi-shuffler secure federated learning (MSFL) framework, based on which we further propound three modules (hierarchical shuffling mechanism, malice evaluation module, and composite defense strategy) to jointly guarantee strong privacy protection, efficient poisoning resistance, and agile adversary elimination. Extensive experiments on standard datasets exhibited the method's effectiveness in thwarting different FL poisoning attack paradigms with a minimal cost of privacy breaches.

CHALLENGES AND OPPORTUNITIES TO TEACHING INQUIRY APPROACHES BY STE(A)M PROJECTS IN THE PRIMARY EDUCATION CLASSROOM

This study analyses the teachers' perceptions of their capacity for designing and developing STE(A)M projects in a professional development programme (PD) conducted through a collaborative educational research project carried out between the University and the centres by the IndagaSTEAM Escuela project. Incorporating STEAM education in the classroom poses various challenges to teachers, among other aspects, associated with conceptualising, designing and applying the curricular integration of STE(A)M subjects, requiring training programs updates for this purpose. Hence, the design developed promotes applying an inquiry-based approach and adapting the integrative STE(A)M curriculum in the Primary Education classroom. Challenges and opportunities of the teachers' involvement have been analysed as a case study undertaken longitudinally between 2019 and 2022. Data have been compiled from a varied typology (rubrics evaluation, ad hoc interviews and questionnaires, using the Likert scale), which give an account of the global perception these educators have, on the one hand, of STE(A)M education and the inquiry approach and, on the other hand, of the collaborative learning scenario from the framework used. Keywords: active learning, primary school teacher, teaching fundamental sciences, teacher skills

CleAR: an interoperable architecture for multi-user AR-based school curricula

Although there are some experiences that demonstrate the validity of the use of augmented reality in schools to help students understand and retain complex concepts, augmented reality has not been widely adopted in the education sector yet. This is in part because it is hard to use augmented reality applications in collaborative learning scenarios and to integrate them in the existing school curricula. In this work, we present an interoperable architecture that simplifies the creation of augmented reality applications, enables multi-user student collaboration and provides advanced mechanisms for data analysis and visualization. A review of the literature together with a survey answered by 47 primary and secondary school teachers allowed us to identify the design objectives of cleAR, an architecture for augmented reality-based collaborative educational applications. cleAR has been validated through the development of three proofs of concept. cleAR provides a more mature technological ecosystem that will foster the emergence of augmented reality applications for education and their inclusion in existing school programs.

Privacy-Preserving Split Learning for Large-Scaled Vision Pre-Training

The growing concerns about data privacy in society lead to restrictions on the computer vision research gradually. Several collaboration-based vision learning methods have recently emerged, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., federated learning and split learning. These methods protect user data from leaving local devices, and make training performed only by uploading gradients, parameters, or activations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . However, there is little research on collaborative learning based on state-of-the-art and large-scaled models, mainly due to the high computation or communication overheads of the latest models. Training these models may be still unrealized for users’ terminals. In this paper, we make a first attempt at the sensitive image pre-training with large-scaled models in the collaborative learning scenario, and propose a new lightweight framework for split learning based on mask, Masked Split Learning (MaskSL). We further ensure its security by differential privacy. Besides, we model the computation and communication overheads of several collaborative learning approaches by deduction to illustrate advantages of our scheme. Finally, we design and conduct a series of experiments on real-world datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., in face recognition and medical image classification tasks, to demonstrate the performance of MaskSL.

Private Data Inference Attacks against Cloud: Model, Technologies, and Research Directions

Machine learning models are established with a variety of data collected from individual users who are concerned about their privacy. Various cloud service providers (e.g., Amazon, Google, Alibaba) commercialize their models by selling them or providing limited access via APIs. However, existing studies have shown that such cloud-based models are susceptible to training data inference attacks (i.e., membership inference attacks, attribute inference attacks, and model inversion attacks) since the trained models remember information about the training data. In this article, we perform an exhaustive investigation on the current training data inference attacks against cloud-based models. According to the attack scenario, we divide the existing inference attacks into two categories: centralized and collaborative learning scenarios. We first give a comprehensive review of attacks in each category and then give a qualitative comparison of the existing attacks. Further, we quantitatively compare the performance of different attack strategies by experiments. Last but not least, we pinpoint some open issues and potential future directions that need to be investigated in this area.

A Student Grouping Method for Massive Online Collaborative Learning

As a brand-new learning strategy, cooperative learning can greatly improve the quality of classroom teaching and the teamwork and learning efficiency of students. The conventional student grouping models for cooperative learning take less consideration of the complementarity of students’ knowledge structures and learning interests, so they are not quite suitable for online teaching, which is provided for a huge number of students and has a large scale of learning resources and great freedom of choice. To this end, this paper studies the student grouping method for massive online collaborative learning. Firstly, the problem of identifying the student’s knowledge state was described and characterized, and a diagnosis model of students’ knowledge state simulating the cooperative learning process was constructed based on the gated recurrent neural network. The creation of an online collaborative learning scenario that considers both the knowledge state and interests of students was regarded as an NP-hard problem, and then, the enhanced particle swarm optimization algorithm was adopted to realize the grouping of students for massive online collaborative learning. The experimental results verified the effectiveness of the model constructed in this paper.

Competencies in Digitalization: An Experiment in an International Course

This research aims to explore competencies in the digital global context through an international experiment conducted at the Veracruzana University in Mexico and the Public University of Navarre in Spain. In this paper, we report test scores of a course from both universities (N = 85). The course was grounded on Industry 4.0 and digital transformation. Students were required to work in international teams through virtual communication and coworking tools. The main findings show that digital competencies though necessary, do not seem sufficient. Additionally, the research identifies the most developed competencies (decision-making and cognitive development competencies) and the competencies that need to be strengthened (digital competencies). From a practical perspective, the international experiment facilitates a collaborative learning scenario.

Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning

AbstractIn this study, we aim to bridge the gap between the theoretical understanding of attacks against collaborative machine learning workflows and their practical ramifications by considering the effects of model architecture, learning setting and hyperparameters on the resilience against attacks. We refer to such mitigations asmodel adaptation. Through extensive experimentation on both, benchmark and real-life datasets, we establish a more practical threat model for collaborative learning scenarios. In particular, we evaluate the impact of model adaptation by implementing a range of attacks belonging to the broader categories of model inversion and membership inference. Our experiments yield two noteworthy outcomes: they demonstrate the difficulty of actually conducting successful attacks under realistic settings when model adaptation is employed and they highlight the challenge inherent in successfully combining model adaptation and formal privacy-preserving techniques to retain the optimal balance between model utility and attack resilience.

Comparison of Collaborative Learning Models to Improve Programming Competence

The purpose of this research is how collaborative learning strategies should be arranged in three classes as Wang &amp;amp; Hwang model class, control class and experimental class with different treatments in Algorithm and Programming courses. Three learning strategies were tested to see students' cognitive abilities in computer programming skills. Three collaborative learning scenarios were tested, namely: 1) conventional collaborative learning 2) problem-based collaborative learning using an online environment and 3) inquiry-based collaborative learning also using an online environment. The results of the t-test with the one-way ANOVA test showed that the pretest results of the students' ability levels were not different because they had not been treated. While the results of the t-test with the posttest t-test results obtained a very significant difference in student final results, namely the control class 71.30, Wang &amp;amp; Hwang model class 73.0 and the experimental class 81.13. The benefit of the results of this study is that collaborative learning with an inquiry approach allows students to transfer knowledge and does not make lecturers the only source of learning

Homogeneous Group Formation in Collaborative Learning Scenarios: An Approach Based on Personality Traits and Genetic Algorithms

The incorporation of collaborative work in the educational field grows day after day, as does the research associated with this topic. One of the most recurring problems faced by teachers who want to employ this learning strategy is the good students’ group formation since this task can be complex both conceptually and computationally, especially if the aim is to automate it. Considering that group formation is key when developing activities in collaborative learning scenarios, this article aims to propose a technique based on a genetic algorithm approach for achieving homogeneous groups, considering the students personality traits as grouping criteria, since, the aforementioned traits, have proven to be significantly predictive of academic performance and, in addition, to be associated with variables with a strong influence on academic success, such as intelligence and beliefs of self-efficacy. A controlled experiment was designed with 132 students, quantifying the personality traits through the “Big Five Inventory,” forming working groups and developing a collaborative activity in the initial programming courses. The experiment results allowed validation, not only from a computational point of view evaluating the algorithm performance but also from a pedagogical point of view, confronting the results obtained by students: formation based on personality traits versus formation by the students’ preference. The highlight of this article is that the homogeneous groups generated by the proposed approach produced better academic results compared to those obtained by groups generated by the students' preference, at the moment of developing a collaborative activity.

Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

In this paper, an approach based on genetic algorithms is proposed to form groups in collaborative learning scenarios, considering the students’ personality traits as a criterion for grouping. This formation is carried out in two stages: In the first, the information of the students is collected from a psychometric instrument based on the Big Five personality model; whereas, in the second, this information feeds a genetic algorithm that is in charge of performing the grouping iteratively, seeking for an optimal formation. The results presented here correspond to the functional and empirical validation of the approach. It is found that the described methodology is useful to obtain groups with the desired characteristics. The specific objective is to provide a strategy that makes it possible to subsequently assess in the context what type of approach (homogeneous, heterogeneous, or mixed) is the most appropriate to organize the groups.

A Strategy Based on Genetic Algorithms for Forming Optimal Collaborative Learning Groups: An Empirical Study

Considering that group formation is key when developing activities in collaborative learning scenarios, this paper aims to propose a strategy based on a genetic algorithm approach for achieving optimal collaborative learning groups, considering the students’ personality traits as grouping criteria. A controlled experiment was designed with 238 students, quantifying their personality traits through the “big five inventory” (BFI), forming working groups and developing a collaborative activity in programming and related courses. The experiment results allowed validation, not only from a computational point of view evaluating the algorithm performance but also from a pedagogical point of view, confronting the results obtained by students applying the proposed approach with those obtained through other group formation strategies. The highlight of the study is that those groups whose formation was pre-established by the teachers through the proposed strategy have generally had a better collaborative performance than the groups with traditional formation, except in the case of heterogeneous formation, at the time of developing a collaborative activity. In addition, through the experiment, it was found that not considering criteria related to personality traits before the group formation generally led to lower results.

Guided participation in youth media practices

Youth has to deal with some digital practices and develop media discourses on their own. Our study aims to deepen these concepts from the point of view of the guided participation, understood in this case as a collaborative process of media literacy based on culturally significant activities. Our data comes from a series of workshops that took place at the Telefonica Flagship Store (Madrid, Spain) with teens between eight and 14 years old. The evidence was collected by qualitative research techniques such as observation, conversation and descriptive analysis. The results give us some preliminary ideas for discussion: 1) social media practices enable youth to connect their online and offline activities with their interests; 2) the generation of collaborative learning scenarios based on the interaction between young people becomes a fundamental element of media literacy and 3) user-generated content emerges as an identity and habits depiction&#x0D; in media, especially among young people.

SENA Tecnoacademia Risaralda and Caldas as a Collaborative Learning Scenario in Robotics

SENA Tecnoacademia Risaralda and Caldas as a Collaborative Learning Scenario in Robotics

ImmerTai: Immersive Motion Learning in VR Environments

Immersive learning in Virtual Reality (VR) environments is the developing trend for future education systems including remote physical training. This paper presents “ImmerTai”, a system that is designed for effective remote motion training, particularly for Chinese Taichi, in an immersive way. With ImmerTai, the Taichi expert’s motion is captured and delivered to remote students in CAVE, HMD and PC environments for learning. The students’ motions are also captured for motion quality assessment and a group of students can form a virtual collaborative learning scenario. We built up a Taichi motion dataset with ground truth of motion quality, and based on this, we developed and evaluated several motion quality assessment methods. Then, user tests were designed and carried out to measure and compare the learning outcomes (learning time, quality and overall efficiency) of students in Cave Automatic Virtual Environment (CAVE), Head Mounted Display (HMD) and Personal Computer (PC) environments. Meanwhile, the connections between students’ learning outcomes and their VR experience were investigated and discussed too. Our results show that ImmerTai can accelerate the learning process of students noticeably (up to 17%) compared to non-immersive learning with the conventional PC setup. However, we observed a substantial difference in the quality of the learnt motion between CAVE (26% gain) and HMD (23% drop) compared to PC (baseline). While strong VR presence can enhance the learning experience of students, their learning outcomes are not fully consistent to their experience. Overall, ImmerTai with CAVE demonstrated a significantly higher learning efficiency than other tested environments.

Learn‐CIAN: A visual language for the modelling of group learning processes

AbstractThe incorporation of advanced information and communication technologies into the field of education has made the design and deployment of courses and instructional units more and more complicated. In order to support such complex task, methods and techniques have been proposed in the last years for the standardization, formalization and modelling of learning scenarios. Thus, several Visual Instructional Design Languages (VIDL) have been developed, with few of them giving an adequate and complete support for group learning processes. In this paper, we outline a domain‐specific language (DSL) for the modelling of collaborative learning scenarios. This DSL is called Learn‐CIAN. This notation has been validated by a sample of the students of Computer Science and Education degrees such that we could obtain a first view about the understandability and acceptance of this language from potential final users.

Using collaborative learning scenarios to teach programming to non‐CS majors

AbstractThis article presents a new approach based on “collaborative scenarios” to enhance teaching–learning programming in non‐Computer Science oriented curricula. In this context, a literature review of tools related to teaching programming since a collaborative approach is presented. The collaborative scenarios was supported by a platform called TASystem, and applied in a case study carried out with students from Topographic Engineering Department at the Universidad del Valle (Colombia). The students’ social interaction was analyzed with Social Network Analysis and Content Analysis techniques and so was the students’ performance. The results showed an improvement in the students’ performance and an increment in the social relationship among the students.

Scaffolding Collaborative Learning in Pairs within a Technology-Enhanced Learning Environment

Previous research shows that scaffolding is an effective method to foster learning in technology-enhanced learning environments demanding self-regulation. This study considers the option of using a scaffold in an online learning environment working with a partner to solve a multiple-choice assessment. A scaffolding sequence was designed in which subjects worked in pairs. Then the collaborative and individual learning performances were measured and compared. Using the cognitive load theory (CLT), variables were defined to explain effects on performance. Participants in the experiment included 41 university students. Findings reveal that learners learning in the collaborative learning scenario performed better than within the individual learning scenario. Results show that three interrelated variables, mental effort, task difficulty and motivation affected performance.