Different Types Of Learning Research Articles

Working With (Not Against) the Technology: GPT3 and Artificial Intelligence (AI) in College Composition

The use of artificial intelligence (AI) for improvement of writing is commonplace with word-processing software and cloudbased writing assistants such as Grammarly and Microsoft Word. However, more and more options are cropping up that move beyond assistance with grammar, spelling, and punctuation to complete essay generation. The free availability of AI essay generators has led to lamenting the coming death of college writing. But AI has been used in the previously noted examples for decades without such a reaction. In fact, the idea that the use of essay generating software is synonymous with academic dishonesty is as passé as worries about allowing students to use calculators or chalkboards. Both are tools that emerged by affording students a different type of learning which was not rote memorization. The questions now become how AI tools can and should be used to teach English composition and to what extent. In the conceptual age where AI is used to augment all other facets of human creativity, providing students with the tools they will need for effective communication becomes inevitable. These new AI tools may allow students to master grammar and syntax more quickly in order to move on to important research questions that will contribute to knowledge in their given fields. This study investigates the current and potential uses of AWE, AAG and AI essay generators in a first-semester English composition classroom. Students in the study were provided with the same assignments and learning outcomes as are standard in English, composition courses but were encouraged to use AI applications when prompted to discover the usefulness and limitations of such technology. Results from the study confirm that use of such tools does not automatically lead to plagiarism or academic dishonesty. On the contrary, higher-order thinking skills and metacognition are required to use AI tools appropriately to learn writing skills. Furthermore, the tools themselves became the topic covered in the class for the study and led to further social and ethical implications

Self-supervised Learning: A Succinct Review.

Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article.

A REVIEW OF INCIDENTAL LEARNING OF VOCABULARY AND COLLOCATION IN DIFFERENT MODES OF INPUT

More and more attention has been paid to the incidental learning of vocabulary and multiword expressions in different modes of input in recent years. This paper reviews empirical studies that have examined the incidental learning of vocabulary and multiword expressions in four modes of second language input (reading, listening, watching, and reading while listening). It was found that most studies concentrated on the learning of vocabulary and collocations rather than other types of multiword expressions. More attention should be paid to the incidental learning of different types of multiword expressions in future studies.

Cumulative advantage and learning in mid-life

ABSTRACT This paper draws on longitudinal birth cohort data for Britain to analyse participation in learning activities by people in their 30s and 40s. People in this age group have received less attention than either young adults or people in retirement. Yet technical change and the need for new skills make it important for them to engage in learning to improve their prospects at work. We investigate participation and non-participation in a range of different types of learning including gaining qualifications, vocational training and learning for interest. Statistical models explore how factors which occur before their 30s influence learning in this phase of the lifecourse. The results show that cumulative advantage is important – those with the highest qualifications in early adulthood were most likely to engage in further learning later on. Participation in learning activities of any kind in young adulthood was also a key antecedent factor predicting higher chances of participating in learning in mid-life.

A transformational learning framework for sustainable entrepreneurship education: The power of Paulo Freire's educational model

This paper responds to the paucity of theorisation on sustainable entrepreneurship education (SEE) to date. By virtue of its potential commitment to transforming socio-economic relations, we argue that Freire's critical pedagogical approach offers a basis for a theory of SEE that has the potential to be transformational at the individual, organisational and societal level. We show how Freire's approach, which has already found some application in entrepreneurship education (EE), can be complemented in two ways for the purpose of SEE. Firstly, through the addition of Shotter's withinness (rather than aboutness) thinking, which provides a rationale for dialogue with practitioners as a means of developing a subjectively informed understanding of sustainable entrepreneurship in practice. Secondly, through Sipos, Battisti & Grimm's Head, Hands and Heart approach to transformational learning in sustainability education, which provides a practical guide to the different types of learning that are involved in such transformation. We argue that the cognitive (Head), psycho-motor (Hands) and affective (Heart) domains of learning are essential in practical SEE that aims to bring profound changes in knowledge, skills and attitudes, transforming mindsets of both learners and educators in line with the new socio-economic, environmental challenges that our societies face.

A Comparative Review over Online Modes of Learning and Collaborative Blended Learning

It is aimed at determining the significant predictors of blended learning effectiveness taking student characteristics/background and design features as independent variables and learning outcomes as dependent variables. Multiple regression analysis results showed that blended learning design features (technology quality, online tools and face-to-face support) and student characteristics predicted student satisfaction as an outcome. The results indicate that some of the student characteristics/backgrounds and design features are significant predictors for student learning outcomes in blended learning. In this paper we discussed the different types of blended learning and we comparatively review with different types of learning i.e. flipped learning, online and hybrid learning.

Many Destinations, Many Pathways: A Quantitative Analysis of Legitimate Peripheral Participation in Scratch

Although informal online learning communities have proliferated over the last two decades, a fundamental question remains: What are the users of these communities expected to learn? Guided by the work of Etienne Wenger on communities of practice, we identify three distinct types of learning goals common to online informal learning communities: the development of domain skills, the development of identity as a community member, and the development of community-specific values and practices. Given these goals, what is the best way to support learning? Drawing from previous research in social computing, we ask how different types of legitimate peripheral participation by newcomers-contribution to core tasks, engagement with practice proxies, social bonding, and feedback exchange-may be associated with these three learning goals. Using data from the Scratch online community, we conduct a quantitative analysis to explore these questions. Our study contributes both theoretical insights and empirical evidence on how different types of learning occur in informal online environments.

How common is a common error term? The rules that govern associative learning in sensory preconditioning and second-order conditioning.

In standard (first-order) Pavlovian conditioning protocols, pairings of an initially neutral conditioned stimulus (CS) and a biologically significant unconditioned stimulus (US) result in the formation of a CS-US association. The strength of this association is theoretically regulated by prediction error: specifically, the difference between the total level of conditioning supported by the US and the degree to which it is predicted by all stimuli present (i.e., a common error term). In higher-order conditioning protocols (e.g., sensory preconditioning and second-order conditioning), a Pavlovian CS is used to condition responses to other stimuli with which it is paired. At present, it is unknown whether error-correction processes regulate associative learning in higher-order conditioning and, if so, whether these processes are the same as those that regulate formation of a CS-US association in first-order conditioning. Here we review studies that have provided findings relevant to this question: specifically, studies that have examined blocking and/or inhibitory learning in sensory preconditioning and second-order conditioning. These studies show that: (1) animals can form inhibitory associations between relatively neutral sensory stimuli; (2) the learning that occurs in sensory preconditioning and second-order conditioning can be blocked; and, finally, (3) a first-order CS can block conditioning to a second-order CS, and vice versa. The findings are taken to imply that a common error term regulates associative learning in higher-order conditioning, just as it regulates associative learning in first-order conditioning. They are discussed with respect to the nature of the error signal that underlies conditioning and future work that is needed to advance our understanding of the rules that govern different types of learning.

Layered input GradiNet for image denoising

In image denoising, the recovery of high-frequency regions, such as image edges, directly affects the quality of the denoised images. However, previous deep learning-based denoising methods fail to effectively allocate the transmission of different frequency information and have difficulty giving network attention to high-frequency regions. In this paper, we rethink the fusion of image gradients in a neural network and deeply mine the intrinsic structure of the input image to propose a novel layered input gradient network (LIGN) for image denoising. The core of our network focuses on the features of different frequencies through two networks, which contain several key elements: (a) The input noise image is layered to widen the shallow layer of the network and to promote the hierarchical learning of different types of frequencies. (b) A multiscale feature extraction (MFE) block and information shunting (IS) block are proposed to integrate and separate various frequency features. (c) A gradient network (GradiNet) is designed to extract high-frequency information by network training, and the information is adaptively added to the input of the parallel main network (MainNet) through normalization to obtain high-quality images. Furthermore, we propose a sharpening loss function to enhance the texture details of the denoised image and improve visual quality. Extensive experiments on synthetic and real-world datasets show that the proposed method greatly enhances perceptual visual quality and achieves state-of-the-art performance on both PSNR and SSIM. The source code and pretrained models are available at https://github.com/JerryYann/LIGN.

Association between Self-Efficacy and Learning Conformity among Chinese University Students: Differences by Gender

Background: Although past research highlights the impact of self-efficacy on university students’ learning motivation, examining potential links with specific types of learning conformity is limited. The current study examined associations between Chinese university students’ perceived self-efficacy and learning conformity across different types of learning motivation.Methods: A total of 339 Chinese university students were surveyed using the General Self-Efficacy Scale and the Learning Conformity Scale. Multiple regression models were constructed to focus on the mechanisms of general self-efficacy on learning conformity. Results: (1) There are three types of learning conformity: learning abidance, learning obedience, and learning compliance. (2) General self-efficacy has a negative effect on learning obedience. In contrast, it positively affects learning abidance and learning compliance. (3) The general self-efficacy of girls is lower than that of boys. Still, girls are more likely to be motivated to learn compliance than boys. Conclusions: The study reveals that it is essential to stimulate students’ motivation to learn abidance to love learning from the inside out; to improve girls’ self-efficacy; to raise students’ awareness of self-respect and self-development; and to encourage self-approval in public institutions.

Competence and autonomous motivation as motivational predictors of college students’ mathematics achievement: from the perspective of self-determination theory

BackgroundApplied Calculus courses serve hundreds of thousands of undergraduates as quantitative preparation and gatekeepers across diverse fields of study. The current study investigated how motivational factors are associated with students’ learning outcomes in Applied Calculus courses from the perspective of self-determination theory—a sound comprehensive motivation theory that has been supported by considerable research in psychology and education. In order to have a nuanced understanding of students’ motivation and learning in Applied Calculus courses, we used three different types of learning measures to investigate students’ mathematics achievement, including course grades, a standardized knowledge exam, and students’ perceived knowledge transferability.ResultsWe tested the relationships between motivational factors and learning outcomes with a multi-semester sample of 3226 undergraduates from 188 Applied Calculus classrooms. To increase the precision of our analysis, we controlled for three demographic variables that are suggested to be relevant to mathematics achievement: gender, minority group status, and socioeconomic status. With a series of multilevel modeling analyses, the results reveal that: (1) competence satisfaction predicts college students’ mathematics achievement over and above the satisfaction of needs for autonomy and relatedness; and (2) autonomous motivation is a more powerful predictor of college students’ mathematics achievement than controlled motivation and amotivation. These findings are consistent across different types of learning outcomes.ConclusionsSelf-determination theory provides an effective framework for understanding college students’ motivation and learning in Applied Calculus courses. This study extends self-determination theory in the field of mathematics education and contributes to the dialogue on advancing undergraduate science, technology, engineering, and mathematics (STEM) education by providing evidence to understand how motivational factors are associated with students’ learning outcomes in undergraduate mathematics courses.

RETRACTED: Multimedia Presentations Through Digital Storytelling for Sustainable Development of EFL Learners’ Argumentative Writing Skills, Self-Directed Learning Skills and Learner Autonomy

The present study explored the impact of online digital storytelling on developing EAP students’ argumentative writing skills, and its impact on improving self-directed learning skills and learner autonomy in language learning. To reach such ends, a standardized pretest and posttest in argumentative writing skills were designed, which depends on the writing tasks of IELTS task 2 and Independent TOEFL test writing tasks. Also, the self-rating scale of self-directed learning and language Learner Autonomy Scale (LAS) have been exploited. The two experimental groups were trained to write using two different types of learning; group A through online storytelling and group B used offline content producing software to produce digital storytelling to promote writing skills. On the other hand, the control group (group C) used a traditional storytelling strategy. Results primarily revealed that the literacy skills of those who produced their stories with the offline software (group B) improved significantly in comparison to the other experimental group which was taught through online digital storytelling (group A). The control group achieved the least of the three groups in developing target skills. Further, the qualitative analysis of experimental group members’ responses in the semi-structured interviews revealed positive attitudes toward learning through blended learning approaches compared with pure online web-based learning. It is, therefore, recommended that blended learning should be used for students from communities with poor digital literacy and technology infrastructure. Also, online learning should be used with care to cope with community needs to fill a real gap in field practices in language learning to improve multiliteracy skills.

“Black CNN”: Cultural Transmission of Moral Norms through Narrative Art

Abstract In recent debates in moral psychology and literary Darwinism, several authors suggested that narrative art plays a significant role in the process of the social learning of moral norms, functioning as storage of locally salient moral information. However, an integrative view, which would help explain the inner workings of this morally educative function of narrative art, is still lacking. This paper provides such a unifying theoretical account by bringing together insights from moral psychology (concerning the non-reflective nature of moral cognition), educational sciences (concerning different types of settings and methods of moral learning), cognitive/evolutionary narratology (concerning how easy narrative art lends itself to the transmission of moral information), and cultural evolution (concerning different types of learning strategies/biases in relation to narrative art). A study of contemporary American rap music is included to showcase the benefits of this integrative approach in the context of one particular type of narrative art.

Deep Learning (CNN) and Transfer Learning: A Review

Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate different types of learning. A number of experiments showed that deep learning systems fared better than traditional ones when it came to image processing, computer vision, and pattern recognition. Several real-world applications and hierarchical systems have utilised transfer learning and deep learning algorithms for pattern recognition and classification tasks. Real-world machine learning settings, on the other hand, often do not support this assumption since training data can be difficult or expensive to get, and there is a constant need to generate high-performance beginners who can work with data from a variety of sources. The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current solutions and evaluate applications in diverse areas of transfer learning as well as deep learning.

Academic library spaces and student activities during the COVID-19 pandemic

A library in higher education plays a primary role in students' learning on campus. In addition to individually-focused studying, students come to a library for various purposes, such as group learning, collaborating, and socializing. To support students' different types of learning, appropriate physical and functional environments of the spaces must be provided. However, the environmental effects of learning spaces have not been explored extensively. Additionally, the COVID-19 pandemic has forced students to remain and study at home for extended periods, and it is expected that the pandemic experience has affected students' space use patterns. This study aims to examine the effect of the pandemic on students' library usage and to investigate the necessary environments to effectively support students' learning activities. Data was collected via interviews with 12 students. One of the main findings is that, even though students used the library less during the pandemic, they expected to use it as much as pre-pandemic or even more after the pandemic. Furthermore, both physical and functional environments were associated with the study performance and wellbeing of the students. Therefore, understanding students' learning activities and preferred environments in a library is critical to providing appropriate spaces supporting students' learning performance and wellbeing.

Through the “Camera Lens”: How Do Students Grasp the Future of Learning via Online Platforms?

During the COVID-19 pandemic, the academic world came into contact with a virtual learning environment that allows students and educators to leave the boundaries of space and time and maintain academic interactions at unrestricted times and sites. After three semesters of remote learning, there is a feeling that the return to universities and to closed spaces will deter students and that they will prefer remote learning. Studying from home spares valuable time, time otherwise wasted in traffic jams, as well as petrol and other expenses. Remote learning gives a feeling of freedom, comfort, flexible time, and a better sense of control over one’s studies than in the classroom. The current study, conducted about one year after the outbreak of the pandemic, examined students’ background variables: gender, years of schooling, marital status, financial and employment status – with the goal of exploring the association between these variables and students’ preference for either face-to-face or digital teaching. It is evident from the research findings that after this experience of e-Learning neither of these two methods shows a clear advantage over the other. Was the e-Learning experience during the crisis a one-time, incidental event? Or perhaps, in light of the crisis, academic institutions should prepare for a different type of learning, one that combines face-to-face with digital learning? The study illuminates an issue that is confronting educational institutions in general and academic institutions in particular, i.e., preparations for teaching and learning in the post-crisis world, after the considerable upheaval to which we were subjected.

Sequence representations after action-imagery practice of one-finger movements are effector-independent

Action-imagery practice (AIP) is often less effective than action-execution practice (AEP). We investigated whether this is due to a different time course of learning of different types of sequence representations in AIP and AEP. Participants learned to sequentially move with one finger to ten targets, which were visible the whole time. All six sessions started with a test. In the first four sessions, participants performed AIP, AEP, or control-practice (CP). Tests involved the practice sequence, a mirror sequence, and a different sequence, which were performed both with the practice hand and the other (transfer) hand. In AIP and AEP, movement times (MTs) in both hands were significantly shorter in the practice sequence than in the other sequences, indicating sequence-specific learning. In the transfer hand, this indicates effector-independent visual-spatial representations. The time course of the acquisition of effector-independent visual-spatial representations did not significantly differ between AEP and AIP. In AEP (but not in AIP), MTs in the practice sequence were significantly shorter in the practice hand than in the transfer hand, indicating effector-dependent representations. In conclusion, effector-dependent representations were not acquired after extensive AIP, which may be due to the lack of actual feedback. Therefore, AIP may replace AEP to acquire effector-independent visual-spatial representations, but not to acquire effector-dependent representations.

Do our children learn enough in Sky Class? A case study: online learning in Chinese primary schools in the COVID era March to May 2020

All human being’s ways of living, working and studying were significantly impacted by the Covid-19 in 2020. In China, the Ministry of Education reacted fast in ensuring that primary school students could learn online at home by promoting the Sky Class program from February 2020. Educators, parents, and students all faced the challenges of adapting to new online teaching and learning environments. In this small-scale case study, Sky Class’s content and the participants’ experiences, will be presented. Four primary school teachers and five primary school students and their parents participated in three-rounds of interviews sharing their perspectives and experiences of online learning. The study showed that the students gained more parental support and that they benefited from using multimedia functions, like replay, in their Sky Classes. However, the majority of participants reported that the students learnt less. By mapping the learning activities and themes from Sky Class against Cope and Kalantzis’ e-learning ecologies, our study found that only ubiquitous learning and multimodal meaning were achieved. We suggest the reason may be that high cognitive learning was not achieved due to less teachers’ supervision, lack of interaction, delayed feedback, shorter learning times and communication. In conclusion, innovative pedagogies, which can foster different types of learning from the e-learning ecologies may overcome the negative aspects reported about Sky Class. Further research is required for implementing online technology as a catalyst for educational change.

The effect of Some Different Types of Learning Within Training Programs in Terms of Self-Determination Theory of Motivation on Developing Self- Academic Identity and Academic Buoyancy and Decreasing of Mind Wandering Among University Students in Egypt

The effect of Some Different Types of Learning Within Training Programs in Terms of Self-Determination Theory of Motivation on Developing Self- Academic Identity and Academic Buoyancy and Decreasing of Mind Wandering Among University Students in Egypt

Specifying and optimizing robotic motion for visual quality inspection

Installation or even just modification of robot-supported production and quality inspection is a tedious process that usually requires full-time human expert engagement. The resulting parameters, e.g. robot velocities specified by an expert, are often subjective and produce suboptimal results. In this paper, we propose a new approach for specifying visual inspection trajectories based on CAD models of workpieces to be inspected. The expert involvement is required only to select – in a CAD system – the desired points on the inspection path along which the robot should move the camera. The rest of the approach is fully automatic. From the selected path data, the system computes temporal parametrization of the path, which ensures smoothness of the resulting robot trajectory for visual inspection. We then apply a new learning method for the optimization of robot speed along the specified path. The proposed approach combines iterative learning control and reinforcement learning. It takes a numerical estimate of image quality as input and produces the fastest possible motion that does not result in the degradation of image quality as output. In our experiments, the algorithm achieved up to 53% cycle time reduction from an initial, manually specified motion, without degrading the image quality. We show experimentally that the proposed algorithm achieves better results compared to some other policy learning approaches. The described approach is general and can be used with different types of learning and feedback signals. • Robot-supported visual quality inspection requires accurate position and velocity tuning. • Human-determined velocities are suboptimal and might reduce the quality of the captured images. • A new approach for specifying spatial and temporal course of visual inspection trajectories based on CAD models of workpieces to be inspected. • A new learning method for the optimization of robot speed along the specified path. • Real-world results show a significant improvement of inspection times with no image quality degradation.