Learning Patterns Research Articles

Simulation of policies to promote pro‐environmental behavior in a dynamic learning model of social networks: Cases of Brazil, the United States, Japan, Germany, China, and India

AbstractPro‐environmental behavior does not seem to diffuse sufficiently in Brazil, China, Germany, Japan, India, or the United States. Based on the current distribution of carbon footprints and learning patterns (DeGroot learning, best practice, and free riding) in social networks in the six countries, what will happen to the diffusion of pro‐environmental behaviors in each country? What policy measures will be most effective? This study aims to simulate the diffusion of pro‐environmental behavior and to test the effectiveness of various policy measures in a dynamic social network model in the six countries. The distribution of pro‐environmental behaviors is generated by global gridded carbon footprint data. The distribution of learning patterns in social networks is estimated in each country, using data from the World Values Survey. This study considers three policy measures: increasing the number of people pursuing best pro‐environmental behavior practices (best practice promotion, [BPP]), decreasing the number of free riders (free‐riding reduction, [FRR]), and converting the learning patterns of people with high degrees of centrality into best practice (centrality‐based best practice promotion, [CBPP]). Interesting findings were threefold. First, there are large differences in the estimated degrees of individual pro‐environmental behavior and the proportions of learning patterns in the six countries. Second, FRR and BPP are effective in promoting pro‐environmental behavior, but the choice of FRR or BPP depends on the initial proportion of free riders. Third, CBPP is the most effective policy of the three in all countries. Centrality in social networks must be considered when implementing intervention.

Enhanced Medical Education for Physically Disabled People through Integration of IoT and Digital Twin Technologies

This research presents an innovative approach to revolutionize IoT service development in medical education, specifically designed to empower individuals with physical disabilities. By integrating digital twin technology, we offer dynamic virtual representations of tangible assets, facilitating real-time simulation, monitoring, and feedback. A unique visual response algorithm has been developed to enhance the processing of visual vector data, resulting in a more efficient IoT service development process. Our method demonstrates superior performance over traditional techniques, particularly in achieving higher intrinsic variable merging values, which is critical for accurate and accessible visualization. The practical applications of this technology are highlighted through case studies that demonstrate how physically disabled students can benefit from interactive and immersive educational experiences. For instance, students can engage with the digital twins of medical equipment, allowing them to practice procedures and gain hands-on experience in a virtual environment without physical barriers. This approach not only improves accessibility but also personalizes learning experiences, adapting to the unique needs of each student. The research underscores the importance of inclusive design in developing IoT services, ensuring higher inclusivity rates and addressing diverse learning patterns. The findings suggest that the integration of IoT and digital twin technologies can significantly enhance medical education, making it more accessible, effective, and inclusive for physically disabled individuals. This study lays the groundwork for future advancements in this field, highlighting the potential for ongoing technological innovations to further transform medical education.

Novel framework for learning performance prediction using pattern identification and deep learning

PurposeEducational data mining (EDM) discovers significant patterns from educational data and thus can help understand the relations between learners and their educational settings. However, most previous data mining techniques focus on prediction of learning performance of learners without integrating learning patterns identification techniques.Design/methodology/approachThis study proposes a new framework for identifying learning patterns and predicting learning performance. Two modules, the learning patterns identification module and the deep learning prediction models (DNN), are integrated into this framework to identify the difference of learning performance and predicting learning performance from profiles of students.FindingsExperimental results from survey data indicate that the proposed identifying learning patterns module could facilitate identifying valuable difference (change) patterns from student’s profiles. The proposed learning performance prediction module which adapts DNN also performs better than traditional machine techniques in prediction performance metrics.Originality/valueTo our best knowledge, the framework is the only educational system in the literature for identifying learning patterns and predicting learning performance.

The crowd in MOOCs: a study of learning patterns at scale

ABSTRACT The increasing availability of learning activity data in Massive Open Online Courses (MOOCs) enables us to conduct a large-scale analysis of learners' learning behavior. In this paper, we analyze a dataset of 351 million learning activities from 0.8 million unique learners enrolled in over 1.6 thousand courses within two years. Specifically, we mine and identify the learning patterns of the crowd from both temporal and course enrollment perspectives leveraging mutual information theory and sequential pattern mining methods. From the temporal perspective, we find that the time intervals between consecutive learning activities of learners exhibit a mix of power-law and periodic cosine function distribution. By qualifying the relationship between course pairs, we observe that the most frequently co-enrolled courses usually fall in the same category or the same university. We demonstrate these findings can facilitate manifold applications including recommendation tasks on courses. A simple recommendation model utilizing the course enrollment patterns is competitive to the baselines with 200× faster training time.

Role of Artificial Intelligence and Machine Learning to Create Predictors, Enhance Molecular Understanding, and Implement Purposeful Programs for Myocardial Recovery.

Heart failure (HF) affects millions of individuals and causes hundreds of thousands of deaths each year in the United States. Despite the public health burden, medical and device therapies for HF significantly improve clinical outcomes and, in a subset of patients, can cause reversal of abnormalities in cardiac structure and function, termed "myocardial recovery." By identifying novel patterns in high-dimensional data, artificial intelligence (AI) and machine learning (ML) algorithms can enhance the identification of key predictors and molecular drivers of myocardial recovery. Emerging research in the area has begun to demonstrate exciting results that could advance the standard of care. Although major obstacles remain to translate this technology to clinical practice, AI and ML hold the potential to usher in a new era of purposeful myocardial recovery programs based on precision medicine. In this review, we discuss applications of ML to the prediction of myocardial recovery, potential roles of ML in elucidating the mechanistic basis underlying recovery, barriers to the implementation of ML in clinical practice, and areas for future research.

The impact of family factors and digital technologies on mental health in university students.

A substantial body of research indicates an increasing prevalence of mental health issues among university students in a range of countries. A number of psychosocial factors have been put forward in the research literature as possible explanations for this persistent decline in psychological wellbeing in higher education. The present study focused on the role of family factors and the use of digital technologies by students. A replication study was conducted at the University of the Americas (Chile) based on a previous study on psychosocial factors of academic learning patterns and mental health of university students at the Complutense University of Madrid (UCM), Spain. A cross-sectional design was employed, using the same questionnaire, plus indicators of most frequently used digital technologies by the students. The questionnaire was administered online at the same time to all incoming students, gathering a sample of 4,523 students. A series of multiple regressions and ANOVAs was conducted to ascertain the extent to which family and digital factors could be identified as predictors of mental health indicators. The most significant findings indicate that high levels of parental protection and control/discipline, and especially the high use of social media and smartphones, are particularly salient factors contributing to mental health problems in the learning process of higher education students. The results suggest strategies to promote wellbeing, with a focus on the psychosocial diversity within an inclusive university community. Social and digital innovation, collective entrepreneurship, and participatory place-building may facilitate networks of artistic, cultural, ecological, and sports spaces to promote the sense of university community. A longitudinal follow-up on the same sample across academic years will reveal the extent to which these wellbeing initiatives are fruitful.

Manhaj al-Takamul al-Teknologiya wa Atsaruhu fi Baramij I’dad Mu’allim al-Lughah al-‘Arabiyyah fi Nigeria

The process of educating Arabic language teachers in the modern era is in great need of digital skills and strategies, and these skills have become diverse, so educational institutions must keep up with these changes, from the rapid changes that are taking place in today's world, which have become part of the reality we live in. This article aims to highlight the importance of using digital applications in training Arabic language teachers, and its impact on the educational process to improve teachers' performance in completing their educational tasks and increasing their level of competence and ability in building the personality of students. This study uses a Qualitative approach with a Literature Review whose data sources come from related books and articles. The results show that Developing teachers' digital skills during training is one of the modern areas that is being discussed intensively in teacher professional development, thanks to the great developments in educational technology and the interest in improving electronic learning and teaching patterns. Although there are differences in defining the digital skills needed by teachers, they revolve around how to use and benefit from technology and its applications in the teaching profession, and this is evolving day by day, which confirms that developing digital skills must be a continuous process. It is also clear to the researcher that the idea of ​​using modern methods and skills in teaching Arabic is not new, but rather an old idea that developed when educators and linguists saw the need for it. This is one of the basic means of teaching Arabic in conditions suitable for every learner, and is an important step that helps in spreading Arabic in various parts of the world, as it makes it easier for those who are interested and interested in Arabic regarding the Islamic religion to learn it

AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI.

Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variational information maximization that enables joint optimization of sampling pattern and reconstruction of MRI scans. We represent the encoder as a non-uniform Fast Fourier Transform that allows continuous optimization of k-space sample locations on a non-Cartesian plane, and the decoder as a deep reconstruction network. Experiments on public 3D acquired MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling for both compressed sensing and deep learning reconstructions. We demonstrate that our data-driven sampling optimization method achieves 4.4dB, 2.0dB, 0.75dB, 0.7dB PSNR improvements over reconstruction with Poisson Disc masks for acceleration factors of R = 5, 10, 15, 25, respectively. Prospectively accelerated acquisitions with 3D FSE sequences using our optimized sampling patterns exhibit improved image quality and sharpness. Furthermore, we analyze the characteristics of the learned sampling patterns with respect to changes in acceleration factor, measurement noise, underlying anatomy, and coil sensitivities. We show that all these factors contribute to the optimization result by affecting the sampling density, k-space coverage and point spread functions of the learned sampling patterns.

Deep learning-based evaluation of photovoltaic power generation

Photovoltaic (PV) power generation has emerged as a rapidly growing renewable energy source. However, the PV system output’s intermittent and weather-dependent nature poses challenges when integrating with the power grid. These challenges manifest as critical issues, including voltage fluctuations, harmonic distortion, and current deviation, making it difficult to accurately predict grid conditions. Moreover, the spatial variability caused by PV system intermittency further complicates the situation. To address these challenges and ensure efficient grid integration, this paper proposes a comprehensive approach encompassing deep learning-based state prediction of PV power output. The paper introduces the utilization of a long short-term memory (LSTM) model, a type of deep learning architecture, for learning patterns from historical PV power generation data and weather forecasts. The LSTM model enables accurate predictions for effective grid management by capturing long-term dependencies in PV power generation data. Real-world PV power generation data was employed to evaluate the proposed approach. The results demonstrated the significant improvement in PV power generation prediction accuracy achieved by the LSTM model compared to traditional methods. The proposed approach offers a promising solution for addressing the challenges associated with PV system integration into the power grid. It enables enhanced grid planning, resource allocation, and protection measures to accommodate the increasing penetration of solar energy harnessed through PV systems and related power electronics interfaces.

Learning patterns among Arab college students: the relationship with academic achievement and the moderating role of cognitive engagement

ABSTRACT This study explored the learning patterns of undergraduate students in Kuwait using Vermunt’s model and investigated the relationships between these patterns, cognitive engagement, and academic achievement. The ILS was used to identify the learning patterns of 563 university students. The three identified learning patterns were active, passive, and undirected, with the passive pattern being the most prevalent among Kuwaiti undergraduates. Active learners used self- and external regulation strategies and managed their learning actively, while passive learners studied for tests to find a job and had idealistic learning expectations. The undirected pattern characterized students who did not have a specific learning strategy and required cooperation for learning. The study found that active and passive patterns were positively correlated with academic achievement. Moreover, cognitive engagement was found to moderate the relationship between learning patterns and academic achievement, with a stronger effect observed for passive learners. The study emphasizes the need to shift teaching practices in Kuwait towards problem-based learning to foster critical thinking and highlights the cultural gap between students and teachers.

Understanding the relations between achievement goals and self-regulated learning: a multiple goal perspective

The primary objective was to investigate the relations between college students’ achievement goals and their engagement in self-regulated learning using a person-centered approach. College students (N = 364) completed surveys that assessed mindset, self-efficacy, anxiety, achievement goals, and self-regulated learning. Latent profile analyses identified three types of achievement goal profiles: high all, high approach, and low performance. Results showed that self-efficacy and anxiety were significantly associated with the achievement goal profiles. Students who reported high levels of self-efficacy but low levels of anxiety were more likely to endorse the high approach profile. In addition, we found significant mean-level differences across the achievement goal profiles for students’ reported engagement in self-regulated learning concurrently (time management, procrastination) and later in the semester (motivational regulation, environment management). Students in the high approach profile demonstrated the most adaptive pattern of self-regulated learning. Overall, findings provide support for a multiple goal perspective that endorsing both mastery-approach and performance-approach goals can be beneficial.

Advancing Virtual Interviews: AI-Driven Facial Emotion Recognition for Better Recruitment

Behavior analysis involves the detailed process of identifying, modeling, and comprehending the various nuances and patterns of emotional expressions exhibited by individuals. It poses a significant challenge to accurately detect and predict facial emotions, especially in contexts like remote interviews, which have become increasingly prevalent. Notably, many participants struggle to convey their thoughts to interviewers with a happy expression and good posture, which may unfairly diminish their chances of employment, despite their qualifications. To address this challenge, artificial intelligence techniques such as image classification offer promising solutions. By leveraging AI models, behavior analysis can be applied to perceive and interpret facial reactions, thereby paving the way to anticipate future behaviors based on learned patterns to the participants. Despite existing works on facial emotion recognition (FER) using image classification, there is limited research focused on platforms like remote interviews and online courses. In this paper, our primary focus lies on emotions such as happiness, sadness, anger, surprise, eye contact, neutrality, smile, confusion, and stooped posture. We have curated our dataset, comprising a diverse range of sample interviews captured through participants' video recordings and other images documenting facial expressions and speech during interviews. Additionally, we have integrated existing datasets such as FER 2013 and the Celebrity Emotions dataset. Through our investigation, we explore a variety of AI and deep learning methodologies, including VGG19, ResNet50V2, ResNet152V2, Inception-ResNetV2, Xception, EfficientNet B0, and YOLO V8 to analyze facial patterns and predict emotions. Our results demonstrate an accuracy of 73% using the YOLO v8 model. However, we discovered that the categories of happy and smile, as well as surprised and confused, are not disjoint, leading to potential inaccuracies in classification. Furthermore, we considered stooped posture as a non-essential class since the interviews are conducted via webcam, which does not allow for the observation of posture. By removing these overlapping categories, we achieved a remarkable accuracy increase to around 76.88% using the YOLO v8 model.

Students' proficiency evaluation: a non-parametric multilevel latent variable model approach

ABSTRACT In higher education, students' assessment has a two-fold aim: (i) evaluate students' proficiency level concerning the topics of a specific course; (ii) identify students' weaknesses throughout the whole learning activity and, if any, relate them to a set of socio-demographic and psychological covariates/predictors. In this vein, this manuscript proposes a multilevel latent class model as an analytic strategy to detect homogeneous groups of students based on their abilities, operationalized according to the following dimensions: Knowledge, Applying knowledge, and Judgment. As a novelty, the proposed model associates each dimension with a first-level latent class variable, which contributes to the identification of a second-level latent class variable that summarizes students' abilities according to the whole learning activity. The presented empirical results are based on Statistics tests covering three different topics and survey instruments administered to students of an introductory Statistics course. The main results show that the model identifies distinct overall patterns of learning and differences according to ability dimensions and topics. Moreover, the study of the relationships between the second-level latent class variable and socio-demographic and psychological covariates helps to characterize and deeply understand the students' profiles, fostering the development of tailored recommendations.

Effects of multisensory stimulation on infants' learning of object pattern and trajectory.

This study investigated whether infants encode better the features of a briefly occluded object if its movements are specified simultaneously by vision and audition than if they are not (data collected: 2017-2019). Experiment 1 showed that 10-month-old infants (N = 39, 22 females, White-English) notice changes in the visual pattern on the object irrespective of the stimulation received (spatiotemporally congruent audio-visual stimulation, incongruent stimulation, or visual-only; = .53). Experiment 2 (N = 72, 36 female) found similar results in 6-month-olds (Test Block 1, = .13), but not 4-month-olds. Experiment 3 replicated this finding with another group of 6-month-olds (N = 42, 21 females) and showed that congruent stimulation enables infants to detect changes in object trajectory (d = 0.56) in addition to object pattern (d = 1.15), whereas incongruent stimulation hinders performance.

Rapid Learning of Temporal Dependencies at Multiple Timescales.

Our environment contains temporal information unfolding simultaneously at multiple timescales. How do we learn and represent these dynamic and overlapping information streams? We investigated these processes in a statistical learning paradigm with simultaneous short and long timescale contingencies. Human participants (n = 96) played a game where they learned to quickly click on a target image when it appeared in one of nine locations, in eight different contexts. Across contexts, we manipulated the order of target locations: at a short timescale, the order of pairs of sequential locations in which the target appeared; at a longer timescale, the set of locations that appeared in the first versus the second half of the game. Participants periodically predicted the upcoming target location, and later performed similarity judgments comparing the games based on their order properties. Participants showed context-dependent sensitivity to order information at both short and long timescales, with evidence of stronger learning for short timescales. We modeled the learning paradigm using a gated recurrent network trained to make immediate predictions, which demonstrated multilevel learning timecourses and patterns of sensitivity to the similarity structure of the games that mirrored human participants. The model grouped games with matching rule structure and dissociated games based on low-level order information more so than high-level order information. The work shows how humans and models can rapidly and concurrently acquire order information at different timescales.

Google Classroom Based Arabic Language Learning at Madrasah Tsanawiyah Negeri 5 Tulungagung

The need for learning on digital applications or platforms is currently very high. Arabic language learning is entering an era of industrialization where access to learning already uses applications or digital platforms. The use of contemporary applications such as Google Classroom will change learning patterns from paper-based to paperless. This article aims to explain and analyze Arabic language learning based on Google Classroom media which has been carried out at Madrasah Tsanawiyah Negeri 5 Tulungagung. Employing a qualitative approach, this study analyzed descriptive data in the form of speech or writing and the observable behavior of the research subject. Data were obtained from interviews, observations, and documentation, where the authors conducted research by going directly to the field to find data at Madrasah Tsanawiyah Negeri 5 Tulungagung. The findings showed that learning Arabic using the online model via Google Classroom is very effective and efficient. Google Classroom becomes an alternative media to create changes in learning, it changes the learning environment which was originally traditional to an effective activity by utilizing facilities and attractive media to produce the maximum learning outcomes. This research is expected to provide a scientific contribution as a solution to the learning process in the digital era. The researcher advises future researchers that there is still a lot of online media that can be analyzed further.

Revolutionising the Indian Government School System with the Help of AI and other New Age Technological Developments

Education is essential for individuals to participate in a democratic society, enjoy their rights and fulfil their responsibilities. -John Dewey For a man to be able to negotiate a community life in fulfilling ways and develop a sense of autonomy is the elementary purpose of education. Hence a strong public education system is a requisite for the needs of all, even for the poor and marginalised sections of the society. The norm followed in India is to run behind private education institutes and run away from government schools because of the inadequate infrastructure and demotivated staff. Hence to break these shackles of stereotype mindset, implementing AI at government schools to maximise the quality of education and reach every part of the number one populous country stands important. Not just reaching a large number but also to alter the age-old pattern of learning, with the aid of new technologies and AI the learning process could be given a new direction where the needs of each learner will be met based upon their grasping and understanding of the subject. AI will also play a crucial role in beating the shortage of staff. Private organisations have adopted VR and AR to create immersive and engaging learning experiences, while other new-age technologies such as cloud computing, learning analytics, and blockchain are also used to achieve monopoly in the private education industry. The Indian government should invest in developing and implementing AI-powered learning platforms and other new-age technologies in government schools to serve every Indian who can not afford private institutions. The government should also provide training to teachers on how to use these technologies effectively with certification. By doing so, the Indian government can help to ensure that all students have access to a high-quality modern education. For a country as massive as India in terms of population and democracy both, implementing it is not an easy task, but if changes are made in the legislation to revolutionise the Government school system in India , days are not far where AI will take the whole education system on to an affirmative path. This whole research paper is an analysed perspective of the same topic.

Student Performance Analysis: A Systematic Research

In the rapidly evolving landscape of education, understanding and improving student performance is of paramount importance. This research paper explores the application of Machine Learning (ML) technologies within the realm of Big Data Analytics to comprehensively analyze and enhance student performance. Leveraging the vast amount of data generated within educational institutions, this study demonstrates how ML algorithms and techniques can be harnessed to gain insights into student learning patterns, predict academic outcomes, and develop data-driven strategies for educational improvement. The paper begins by highlighting the significance of student performance analysis in modern education and the challenges faced by institutions in managing and interpreting the growing volume of educational data. It then presents a comprehensive review of the state-of-the- art ML algorithms and data processing techniques relevant to student performance analysis. Furthermore, the research outlines a novel framework for student performance analysis, integrating various ML models such as regression, classification, and clustering, alongside advanced data preprocessing techniques. The proposed framework is designed to handle diverse educational datasets, including academic records, attendance records, socio-demographic information, and learning resources utilization[1] . Through a series of experiments and case studies, this paper demonstrates the practical application of ML in predicting student performance accurately, identifying at-risk students, and personalizing educational interventions. It also delves into the ethical considerations and data privacy concerns associated with the use of student data in ML-based educational analytics. The results and insights from this research offer valuable implications for educational institutions, policymakers, and researchers alike. By harnessing the power of Big Data and ML technologies, institutions can make data-driven decisions to enhance teaching methodologies, improve student support systems, and ultimately elevate student success rates. Additionally, this study contributes to the ongoing discourse on the responsible use of data in education, emphasizing the importance of transparency, fairness, and ethical considerations. In conclusion, this research paper presents a robust framework that showcases the potential of Machine Learning technologies within the realm of Big Data Analytics for student performance analysis. It offers a roadmap for educational institutions to harness the power of data to foster better learning outcomes and contributes to the ongoing dialogue on responsible data usage in education.

Mapping brain development against neurological disorder using contrastive sharing

Cerebral palsy (CP) is a neurological disorder caused by cerebral ischemia and hypoxia during fetal brain development. Early intervention in CP patients has a favorable impact on medications and therapy; however, challenges are associated with early age development, potential recovery, and neuroimaging sensitivity. Previously, the main focus was given on early diagnosis, brain injury characterization, and neurodevelopmental outcomes in infants; however, it is inevitable to study the brain developmental patterns in health controls (HC) and CP children. This study elaborates on the effects of aging on the brains of infants with HC and CP, age ranging from a few months and 17 years using deep learning (DL). The introduced DL-based models, namely SCT-DL and MCT-DL, trained on single contrast MRI (SC-MRI) and coupling of multi-contrast (MC-MRI) to learn vulnerable brain regions associated to brain development. The proposed models are equipped with fully-convolutional-based attention mechanism leveraging average pooling operation, directly passing salient features, parallel partial computing unit, specialized parameter sharing module, fusion, and spatial-channel attention with traversal placement to learn and associate brain development accurately. In addition, the study reports optimal SC-MRI and MC-MRI deeply associated with CP vulnerabilities and association to brain development. The SCT-DL outperformed the counterpart models with accumulative MAE = 1.73 (C11C denotes HC and P denotes CP.= 1.61 and P = 1.63) underlying T1-w, where the MCT-DL is the improved version of SCT-DL and the prediction accuracy reached to MAE = 1.08 (C = 1.01 and P = 1.12) over the coupling of T1-w⊕Sagittal. The trained models result in unmatching and distinct learning patterns for healthy and CP patients. Notably, the age-wise brain development-based results deduce a significance in age association at an early age (around two years) and poor at a later age. The study findings will assist neurodevelopmental processing and clinical practices in radiomics without concerning infants’ uncooperative movement or MRI artifacts.

NATIONAL EDUCATION POLICY 2020: PRACTICAL IMPEDIMENTS AND CHALLENGES

Quality education has been crucial and in demand in a rapidly changing global scenario. With the advent of new theories like constructivism in education, the academicians and the theorists believe that learners develop their understanding through experience rather than by stimulus and response which were the core concepts in the traditional behaviorist theories. National Education Policy 2020 (NEP 2020) is a document that seeks to make education equitable, inclusive and sustainable. The Policy advocates flexibility for the learners, to opt for the learning trajectories and programs, no hard separation between different domains of studies and multidisciplinary education. The policy has proposed to bring structural changes to the education system. The present paper is an attempt to foreground the challenges and hindrances to implementing and practicing a few of the structural changes and aspects. NEP 2020 states that the students will be able to choose many of the subjects and the level in which they take the Board examination- Medium level and Advanced level. The student will select different subjects for study in class 9th. This may invite more challenges relating to infrastructure, conducting examinations, recruiting teachers, etc. Emphasis on the mother tongue as a medium of instruction may be a problem for the children of people in transferable jobs, this means the private English Medium Schools will keep on flourishing. As per the guidelines the Foreign Colleges will be permitted to open campuses in India, however, there is less possibility to get admission in these colleges for the students whose medium of instruction will be regional language. This may again lead to widening the rift between the economically poor and the rich and may invite unequal learning patterns.