Effective Learning Framework Research Articles

Unleashing the secrets of academic writing: exploring Indonesian language courses in higher education

Academic writing has been a widely substantial course in higher education as it plays a vital role in cultivating students’ academic writing skills. Drawing on qualitative descriptive study, this study aims to explore the fundamental requirements for academic writing to base the creation of an effective learning framework. Data consisted of teaching materials, semester learning plan (SLP), assessment tools, and guidelines for academic writing from three universities in Surakarta, Central Java, Indonesia. Findings reveal that learning materials and the SLP have both strengths and weaknesses. Despite these, there is diversity in learning and assessment methods. Adopted teaching methods encompass project-based learning, problemsolving, and lectures, while learning assessment includes portfolio techniques, project assessment, and written exams. These findings provide insight into the imperative of academic writing in the academic setting. This serves as a basis for the development of more effective learning methods. Thus, the proposed model for academic writing adopts a project-based approach to improve students’ writing by integrating indigenous wisdom to promote students' character development.

GraphMoCo: A graph momentum contrast model for large-scale binary function representation learning

In the field of cybersecurity, the ability to compute similarity scores at the function level for binary code is of utmost importance. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale Binary Code Similarity Detection (BCSD). To overcome the aforementioned problems, we propose GraphMoCo: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. We adopt an unsupervised learning strategy. Our approach eliminates the need for manual labeling. By leveraging the intrinsic structural information at multiple levels of the binary code, our model could achieve higher accuracy with a simple CNN-based model. By introducing the preshuffle mechanism, the issue of information leakage in graph momentum update algorithm is mitigated. The evaluation results indicate that GraphMoCo exhibits superior performance compared to SOTA approaches in the function pair search task, showing an average improvement of 7% on AUC, and 10% on MRR and Recall@1. Furthermore, GraphMoCo achieves a MAP of 0.93 on the more challenging dataset 2, which comprises a larger function pool. In a real-world scenario, specifically in known vulnerability searching, GraphMoCo achieves a MRR that surpasses existing SOTA models by 5%.

Predicting Critical Nodes in Temporal Networks by Dynamic Graph Convolutional Networks

Many real-world systems can be expressed in temporal networks with nodes playing different roles in structure and function, and edges representing the relationships between nodes. Identifying critical nodes can help us control the spread of public opinions or epidemics, predict leading figures in academia, conduct advertisements for various commodities and so on. However, it is rather difficult to identify critical nodes, because the network structure changes over time in temporal networks. In this paper, considering the sequence topological information of temporal networks, a novel and effective learning framework based on the combination of special graph convolutional and long short-term memory network (LSTM) is proposed to identify nodes with the best spreading ability. The special graph convolutional network can embed nodes in each sequential weighted snapshot and LSTM is used to predict the future importance of timing-embedded features. The effectiveness of the approach is evaluated by a weighted Susceptible-Infected-Recovered model. Experimental results on four real-world temporal networks demonstrate that the proposed method outperforms both traditional and deep learning benchmark methods in terms of the Kendall τ coefficient and top k hit rate.

Gamification pedagogy: A motivational approach to student-centric course design in higher education

This article explores the praxis of gamification pedagogy and post-secondary course design. The literature on gamified design theory and current research on its application as a pedagogy are explored. A student-centric, motivationally based gamification design model is proposed, operationalized, implemented, evaluated, and reiterated. The design process, application strategies and challenges, and resulting qualitative outcomes over a two-year implementation period of the re-designed gamified course are detailed. Student evaluations rated both the overlaid gamified structural design and the integrated course mechanics as highly motivating and contributing significantly to their success and positive learning experience. The gamified course design was able to resolve historical challenges for the identified course and increased student engagement. Gamification pedagogy proved uniquely effective for two sub-groups of students, those struggling with anxiety and second language learners. This innovative pedagogy effectively leveraged students’ unique intrinsic and extrinsic motivators to create an empowering, supportive, and highly effective learning framework.

Artificial Learning for Part Identification in Robotic Disassembly Through Automatic Rule Generation in an Ontology

With the increasing concern for sustainable treatment of waste electrical and electronic equipment (WEEE), methods of robotic disassembly of WEEE to address various challenges of handling end-of-life products has been a trend in research. The main challenge for robotic disassembly is the uncertainties of product structures, models, and conditions. The ability of a robotic disassembly system to learn new product structures and reason about existing knowledge of product structure is vital to addressing this challenge. This paper presents an effective learning framework and demonstrates the system’s ability to learn relevant information for the disassembly of LCD monitors. The learning algorithm uses a database of previous disassembly experience of the product family and analyses it to create rules and relations between the components and disassembly concepts before expanding the generic ontology for future disassembly runs. The results show a significant increase from 11% to 87% in successful part identification of LCD monitors after being trained on past disassembly experience. The proposed method can greatly aid robotic disassembly of any product family. Note to Practitioners—Robotic systems struggle to disassemble electronic waste due to the complexity and uncertainties in end-of-life products and variations in models and parts. An artificially intelligent method is proposed to enable a robotic disassembly system to address these uncertainties. The method uses a computing technique resembling the cognitive reasoning of a human mind in the form of a map of disassembly concepts connected by relationships. Artificial learning by the robotic system occurs by collecting data from previous disassembly runs of a product, analyzing the data, and expanding the map of knowledge with new concepts and relational rules found. The approach is tested on the robotic disassembly system’s ability to identify parts of LCD monitors which possess uncertainties. An improvement from 11% of successful part identification to 87% is found, which demonstrate that learning has taken place. This approach will be implemented in a larger robotic disassembly system and tested with real robotic disassembly runs in the near future.

Lifelong Age Transformation With a Deep Generative Prior

In this paper, we consider the lifelong age progression and regression task, which requires to synthesize a persons appearance across a wide range of ages. We propose a simple yet effective learning framework to achieve this by exploiting the prior knowledge of faces captured by well-trained generative adversarial networks (GANs). Specifically, we first utilize a pretrained GAN to synthesize face images with different ages, with which we then learn to model the conditional aging process in the GAN latent space. Moreover, we also introduce a cycle consistency loss in the GAN latent space to preserve a persons identity. As a result, our model can reliably predict a person's appearance for different ages by modifying both shape and texture of the head. Both qualitative and quantitative experimental results demonstrate the superiority of our method over concurrent works. Furthermore, we demonstrate that our approach can also achieve high-quality age transformation for painting portraits and cartoon characters without additional age annotations.

Gamification in Cybersecurity Education: The RAD-SIM Framework for Effective Learning

The effectiveness of gamification in educational technology and digital learning environments has been the subject of research for over a decade. Gamified interventions encourage active learning by promoting role-playing, mentalisation, and experimentation. Learner-centred approaches have been shown to increase learning outcomes compared to traditional methods in some domains. Although the behavioural drivers associated with gamified learning are now well documented, practical advice for game designers who produce workplace training is much less common. This paper proposes the RAD-SIM framework for behaviourally effective cybersecurity games design, whereby psychological and behavioural principles are combined with learning theories to facilitate practical game-based learning. Using the framework will increase the engagement and retention of knowledge of the end-user. Although designed with cybersecurity training in mind, there are implications for educational game design in general. Future avenues for research, including potential case studies in practice, increasing the customisability of gamified interventions, and utilising natural data from organisations are considered. Social Machines conducted this work funded by the Research Institute for Sociotechnical Cyber Security (RISCS).

Finger knuckle print recognition for personal authentication based on relaxed local ternary pattern in an effective learning framework

Finger knuckle print (FKP) as a physiological trait with a small image dimension, also a highly distinctive pattern, can be used as a reliable biometric identifier. In this paper, a new effective biometric authentication system using FKP texture based on relaxed local ternary pattern (RLTP) is presented. To further improve performance, cascading, overlapped patching and uniform rotation invariant pattern selection are used. Also to obtain more discriminative dominant patterns, an efficient learning framework is integrated with RLTP feature vectors. Identification and verification experiments conducted on the standard PolyU FKP dataset show the effectiveness of the proposed scheme.

Sentiment analysis via dually-born-again network and sample selection

Text sentiment analysis is an important natural language processing (NLP) task and has received considerable attention in recent years. Numerous deep-learning based methods have been proposed in previous literature in terms of new deep neural networks (DNN) including new embedding strategies, new attention mechanisms, and new encoding layers. In this study, an alternative technical path is investigated to further improve the state-of-the-art performance of text sentiment analysis. An new effective learning framework is proposed that combines knowledge distillation and sample selection. A dually-born-again network (DBAN) is presented in which the teacher network and the student network are simultaneously trained through an iterative approach. A selection gate is defined to deal with training samples which are useless or even harmful for model training. Moreover, both the DBAN and sample selection are further improved by ensemble. The proposed framework can improve the existing state-of-the-art DNN models in sentiment analysis. Experimental results indicate that the proposed framework enhances the performances of existing networks. In addition, DBAN outperforms existing born-again network.

Globalized Multiple Balanced Subsets With Collaborative Learning for Imbalanced Data.

The skewed distribution of data brings difficulties to classify minority and majority samples in the imbalanced problem. The balanced bagging randomly undersampes majority samples several times and combines the selected majority samples with minority samples to form several balanced subsets, in which the numbers of minority and majority samples are roughly equal. However, the balanced bagging is the lack of a unified learning framework. Moreover, it fails to concern the connection of all subsets and the global information of the entire data distribution. To this end, this article puts several balanced subsets into an effective learning framework with a criterion function. In the learning framework, one regularization term called RS establishes the connection and realizes the collaborative learning of all subsets by requiring the consistent outputs of the minority samples in different subsets. Besides, another regularization term called RW provides the global information to each basic classifier by reducing the difference between the direction of the solution vector in each subset and that in the entire dataset. The proposed learning framework is called globalized multiple balanced subsets with collaborative learning (GMBSCL). The experimental results validate the effectiveness of the proposed GMBSCL.

Complete Random Forest Based Class Noise Filtering Learning for Improving the Generalizability of Classifiers

The existing noise detection methods required the classifiers or distance measurements or data overall distribution, and ‘curse of dimensionality’ and other restrictions made them insufficiently effective in complex data, e.g., different attribute weights, high-dimensionality, containing feature noise, nonlinearity, etc. This is also the main reason that the existing noise filtering methods were not widely applied and formed an effective learning framework. To address this problem, we propose here a complete and efficient random forest method (CRF) specifically for the class noise detection by simulating the grid generation and expansion. The CRF is not based on distance measures or overall distribution or classifiers; besides, the voting mechanism makes it able to effectively process datasets containing feature noise. Furthermore, we introduce CRF based class noise filtering learning framework (CRF-NFL) and derive its mathematical model. The framework is then applied to many widely used classifiers including some state-of-the-art algorithms, e.g., k-means tree, GBDT, and XGBoost. Moreover, its parallelized is designed for large-scale data. The CRF-NFL show much better generalizability than the conventional classifiers and the relative density-based method, which is the most effective noise filtering method as far as we know. All research has formed an open source library, called CRF-NFL: http://www.cquptshuyinxia.com/CRF-NFL.html.

Art and science: Promoting understanding and empathy through film

This article introduces a cross-disciplinary collaboration that has brought together the disciplines of film (art) and midwifery (art and science) in the making of a creative documentary to evaluate new learning opportunities and reflective practice for student midwives. The case study film, The Golden Window, was filmed in a neonatal intensive care unit over 72 hours and screened to a group of second-year midwifery students in the Cambridge Arts Picturehouse, Cambridge, before their placements in neonatal intensive care. The students recorded their thoughts and impressions of the film. Their responses are summarised and reflected upon within this paper, with the underlying intent to examine whether this innovative interaction between art and science can provide an appropriate pedagogic framework for effective learning through creative and artistic means.

Success of Student Internship in Engineering Industry

Student internship plays a major role in transforming the engineering interns to ready-to-use professionals. Learning at the workplace has become a challenge for the interns due to several issues. A knowledge gap analysis has been depicted considering all stakeholders of the internship, including the intern, faculty, institution and the industrial organization. This article identifies and discusses these issues and provides a framework for effective learning and above-mentioned transformation considering the engineering industry. The role of the faculty is very important to successfully pursue an internship programme. The article also discusses, in detail, the role of the faculty in the success of the programme.

Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting.

Localizing objects of interest in images when provided with only image-level labels is a challenging visual recognition task. Previous efforts have required carefully designed features and have difficulty in handling images with cluttered backgrounds. Up-scaling to large datasets also poses a challenge to applying these methods to real applications. In this paper, we propose an efficient and effective learning framework called MILinear, which is able to learn an object localization model from large-scale data without using bounding box annotations. We integrate rich general prior knowledge into a learning model using a large pre-trained convolutional network. Moreover, to reduce ambiguity in positive images, we present a bag-splitting algorithm that iteratively generates new negative bags from positive ones. We evaluate the proposed approach on the challenging Pascal VOC 2007 dataset, and our method outperforms other state-of-the-art methods by a large margin; some results are even comparable to fully supervised models trained with bounding box annotations. To further demonstrate scalability, we also present detection results on the ILSVRC 2013 detection dataset, and our method outperforms supervised deformable part-based model without using box annotations.

Immune cooperation mechanism based learning framework

Inspired from the immune cooperation (IC) mechanism in biological immune system (BIS), this paper proposes an IC mechanism based learning (ICL) framework. In this framework, a sample is expressed as an antigen-specific feature vector and an antigen-nonspecific feature vector at first, respectively, simulating the antigenic determinant and danger features in the BIS. The antigen-specific and antigen-nonspecific classifiers score the two vectors and export real-valued Signal 1 and Signal 2, respectively. With the cooperation of the two signals, the sample is classified by the cooperation classifier, which resolves the signal conflict problem at the same time. The ICL framework simulates the BIS in the view of immune signals and takes full advantage of the cooperation effect of the immune signals, which improves the performance of the ICL framework. It does not involve the concept of the danger zone and further suggests that the danger zone is considered to be unnecessary in an artificial immune system (AIS). Comprehensive experimental results demonstrate that the ICL framework is an effective learning framework. The ICL framework based malware detection model outperforms the global concentration based malware detection approach and the local concentration based malware detection approach for about 3.28% and 2.24% with twice faster speed, respectively.

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max–Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC’s ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.

Institutional strategies for supporting learners in a digital age

AbstractThis study examined the policies and practices in nine UK institutions of further or higher education that had made a commitment to supporting students to develop their capabilities to learn in the digital age. Data were collected over a six-month period through multiple interactions with case study sites. Analysis of these data captures institutional practices and the results are mapped onto a developmental framework for effective learning in a digital age.Recommendations are made to institutions considering how best to support their learners, including the need to: specify digital literacies in learning and teaching strategies; prepare students for their experience of learning with technology; reconfigure campus spaces to enhance connectivity and support a range of social learning activities; and create a culture of engaging with students to inform decision-making. These institutional practices aim to support learners to make use of their digital skills and practices. The ultimate aim is to grad...

Developing a Framework for Effective Learning and Teaching in a Secondary School Setting

Developing a Framework for Effective Learning and Teaching in a Secondary School Setting