scholarly journals Career Guidance using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4124-4127
Author(s):  
Shubham Metha
Keyword(s):  
Machine Learning ◽  
Career Guidance ◽  
Career Counselling ◽  
Career Goal ◽  
Domain Experts ◽  
Positive Side ◽  
Respective Field

Career guidance is nowadays become necessary because of its proper planning students are always in positive side and very less chances of failure in their respective field. If Career guidance is efficient then there will be excellent match between student skills and their end career goal for that many career counselling institutes using AI and ML with their domain experts for effective counselling.

scholarly journals A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2514
Author(s):  
Tharindu Kaluarachchi ◽  
Andrew Reis ◽  
Suranga Nanayakkara
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Review Paper ◽  
Learning Systems ◽  
Learning Approaches ◽  
Application Development ◽  
Research Gaps ◽  
Domain Experts ◽  
Working Definition ◽  
Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

scholarly journals Career guidance based on machine learning: social networks in professional identity construction

2020 ◽  
Vol 169 ◽  
pp. 158-163
Author(s):  
Pavel Kiselev ◽  
Boris Kiselev ◽  
Valeriya Matsuta ◽  
Artem Feshchenko ◽  
Irina Bogdanovskaya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Professional Identity ◽  
Identity Construction ◽  
Career Guidance

scholarly journals Human In Command Machine Learning

2021 ◽  
Author(s):  
◽  
Lars Holmberg
Keyword(s):  
Machine Learning ◽  
Human Life ◽  
Design Guidelines ◽  
Significant Other ◽  
Target Domain ◽  
Extraterrestrial Life ◽  
Human Capabilities ◽  
Domain Experts ◽  
Model Training ◽  
Selection Of

Machine Learning (ML) and Artificial Intelligence (AI) impact many aspects of human life, from recommending a significant other to assist the search for extraterrestrial life. The area develops rapidly and exiting unexplored design spaces are constantly laid bare. The focus in this work is one of these areas; ML systems where decisions concerning ML model training, usage and selection of target domain lay in the hands of domain experts. This work is then on ML systems that function as a tool that augments and/or enhance human capabilities. The approach presented is denoted Human In Command ML (HIC-ML) systems. To enquire into this research domain design experiments of varying fidelity were used. Two of these experiments focus on augmenting human capabilities and targets the domains commuting and sorting batteries. One experiment focuses on enhancing human capabilities by identifying similar hand-painted plates. The experiments are used as illustrative examples to explore settings where domain experts potentially can: independently train an ML model and in an iterative fashion, interact with it and interpret and understand its decisions. HIC-ML should be seen as a governance principle that focuses on adding value and meaning to users. In this work, concrete application areas are presented and discussed. To open up for designing ML-based products for the area an abstract model for HIC-ML is constructed and design guidelines are proposed. In addition, terminology and abstractions useful when designing for explicability are presented by imposing structure and rigidity derived from scientific explanations. Together, this opens up for a contextual shift in ML and makes new application areas probable, areas that naturally couples the usage of AI technology to human virtues and potentially, as a consequence, can result in a democratisation of the usage and knowledge concerning this powerful technology.

scholarly journals A Survey of Domain Knowledge Elicitation in Applied Machine Learning

2021 ◽  
Vol 5 (12) ◽  
pp. 73
Author(s):  
Daniel Kerrigan ◽  
Jessica Hullman ◽  
Enrico Bertini
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Development Process ◽  
Model Development ◽  
Knowledge Elicitation ◽  
Future Directions ◽  
Domain Experts ◽  
Applied Machine Learning ◽  
Elicitation Process ◽  
Model Development Process

Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers elicit knowledge from experts in the model development process. We develop a taxonomy to characterize elicitation approaches according to the elicitation goal, elicitation target, elicitation process, and use of elicited knowledge. We analyze the elicitation trends observed in 28 papers with this taxonomy and identify opportunities for adding rigor to these elicitation approaches. We suggest future directions for research in elicitation for machine learning by highlighting avenues for further exploration and drawing on what we can learn from elicitation research in other fields.

The role of the Market in Career Counselling Delivery

1997 ◽  
Vol 6 (3) ◽  
pp. 29-34 ◽  
Author(s):  
Anthony G. Watts ◽  
Tony Watts
Keyword(s):  
Public Policy ◽  
Career Guidance ◽  
Career Counselling ◽  
New Right ◽  
Market Maker ◽  
Guidance Services ◽  
The Public ◽  
The Uk ◽  
And Training

This article explores the roles of public policy in career guidance delivery. Traditionally, most career guidance services have been structured towards the provision of social welfare to the public sector. The New Right critique of this has led to attempts to apply market principles to guidance delivery. This can take the form of a market or quasi-market in guidance. However, guidance can also be viewed as a market-maker: a means of making the labour market and education and training markets work more effectively. Some experiments in applying these principles in the UK and elsewhere are analysed.

scholarly journals Learning similarity measures from data

2019 ◽  
Vol 9 (2) ◽  
pp. 129-143 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Agnar Aamodt ◽  
Kerstin Bach ◽  
Helge Langseth
Keyword(s):  
Machine Learning ◽  
Similarity Measure ◽  
State Of The Art ◽  
Similarity Measures ◽  
Learning System ◽  
Case Based Reasoning ◽  
Training Time ◽  
Domain Experts ◽  
Trained Classifier ◽  
Clustering Data

Abstract Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, datasets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features; thus, they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working toward this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state-of-the-art performance. Finally, the evaluation shows that our fully data-driven similarity measure design outperforms state-of-the-art methods while keeping training time low.

scholarly journals A Research Travelogue on Classification Algorithms using R Programming

2019 ◽  
Vol 8 (4) ◽  
pp. 9155-9158
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Statistical Tests ◽  
Learning Task ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Domain Experts ◽  
R Programming ◽  
Training Examples

Classification is a machine learning task which consists in predicting the set association of unclassified examples, whose label is not known, by the properties of examples in a representation learned earlier as of training examples, that label was known. Classification tasks contain a huge assortment of domains and real world purpose: disciplines such as medical diagnosis, bioinformatics, financial engineering and image recognition between others, where domain experts can use the model erudite to sustain their decisions. All the Classification Approaches proposed in this paper were evaluate in an appropriate experimental framework in R Programming Language and the major emphasis is on k-nearest neighbor method which supports vector machines and decision trees over large number of data sets with varied dimensionality and by comparing their performance against other state-of-the-art methods. In this process the experimental results obtained have been verified by statistical tests which support the better performance of the methods. In this paper we have survey various classification techniques of Data Mining and then compared them by using diverse datasets from “University of California: Irvine (UCI) Machine Learning Repository” for acquiring the accurate calculations on Iris Data set.

scholarly journals Career Guidance and Its Implications for Students’ Career Choices: The Case of Public Senior High Schools in Ghana

2020 ◽  
pp. 62-69
Author(s):  
Joshua-Luther Ndoye Upoalkpajor
Keyword(s):  
High School ◽  
High School Students ◽  
High Schools ◽  
Career Choices ◽  
Career Guidance ◽  
Sampling Technique ◽  
Senior High School ◽  
Career Counselling ◽  
School Students ◽  
The Government

As an individual goes through Senior High School education, s/he encounters situations which require them to take appropriate educational, vocational and social decisions. This highlights the need for guidance and counselling services in learning institutions. Its importance cannot be overstated. Research has shown that young people think about careers within the context of life more than in terms of qualifications and training. This study explored the knowledge of senior high school students in the Agona East district, about career counselling and how career guidance has been of benefit to them. The qualitative approach of descriptive survey using the purposive sampling technique was adopted. The data gathered was analysed using two main themes consisting of several sub-themes. It emerged from the study that for each occupation, different interests, abilities, personality traits and professional values are required. The findings also revealed that career counselling helps students to link what they learn to their interests, capacities, aspirations, and match these with existing opportunities. Going forward, the study recommends that schools                    assist students to identify their interests and make them aware of the importance of academic qualifications in making career choices. It further recommends to policy makers; especially the government of Ghana, to reinforce Guidance and Counselling programmes in senior high                       schools.

scholarly journals Developing Classification Model for Chickpea Types using Machine Learning Algorithms

2020 ◽  
Vol 10 (1) ◽  
pp. 5-11
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Research Center ◽  
Domain Experts ◽  
Selection Mechanisms ◽  
Type Classification

Ethiopia is the leading producer of chickpea in Africa and among the top ten most important producers of chickpea in the world. Debre Zeit Agriculture Research Center is a research center in Ethiopia which is mandated for the improvement of chickpea and other crops. Genome enabled prediction technologies trying to transform the classification of chickpea types and upgrading the existing identification paradigm.Current state of the identification of chickpea types in Ethiopia still sticks to a manual. Domain experts tried to recognize every chickpea type, the way and efficiency of identifying each chickpea types mainly depend on the skills and experience of experts in the domain area and this frequently causes error and sometimes inaccurate. Most of the classification and identification of crops researches were done outside Ethiopia; for local and emerging varieties, there is a need to design classification model that assists selection mechanisms of chickpea and even accuracy of an existing algorithm should be verified and optimized. The main aim of this study is to design chickpea type classification model using machine learning algorithm that classify chickpea types. This research work has a total of 8303 records with 8 features and 80% for training and 20% for testing were used. Data preprocessing were done to prepare the dataset for experiments. ANN, SVM and DT were used to build the model. For evaluating the performance of the model confusion matrix with Accuracy, Recall and Precision were used. The experimental results show that the best-performed algorithms were decision tree and achieve 97.5% accuracy. After the evaluation of results found in this research work, agriculture research centers and companies have benefited. The model of chickpea type classification will be applied in Debre Zeit agriculture research center in Ethiopia as a base to support the experts during chickpea type identification process. In addition it enables the expertise to save time, effort and cost with the support of the identification model. Moreover, this research can also be used as a corner stone in the area and will be referred by future researchers in the domain area.

scholarly journals Career management of special people needs in the labour market

2019 ◽  
Vol 3 (3) ◽  
pp. 11-20
Author(s):  
Katarina Kravos
Keyword(s):  
Special Needs ◽  
Labour Market ◽  
Career Guidance ◽  
Self Determination ◽  
Career Counselling ◽  
Career Management ◽  
Competence Model ◽  
Career Interventions ◽  
Inclusive Approach ◽  
Medical Paradigm

Introduction. The paper reviews the literature on characteristics of labour market and its effect on career of people with special needs. While independent career guidance and management remains difficult for people with special needs, because of the rapid changes in the modern labour market, the evidence shows that their career remains a challenge mostly because of the way they are viewed – they are often viewed through their deficits, disabilities, and illnesses. Aim and tasks. The purpose of this paper is to suggest a new, inclusive perspective in career counselling of persons with special needs and their careers. By abandoning the medical paradigm in career of people with special needs, we focus on advantage competences model and self-determination. Results. It is shown that today’s labour market has become unstable and unpredictable, which can be proven by an increased development of atypical ways of employment. The changes in today’s labour market have also affected the careers of people with special needs, who are additionally faced with possible illnesses, disorders, and other barriers. Therefore, we may expect that they may require more help from career advisers and different approaches in counselling. In addition, characteristics of today’s labour market suggest that we must focus on different career understanding – not as a guidance, as it cannot be further predicted, but as management, to become our own life/career agent. It should not be any different in people with special needs. The area of employing people with special needs has not yet abandon the medical paradigm, which is a barrier for their career management. Thus the new inclusive approach has been developed – an advantage competence model. In the model competences of people with special needs, there are a basis for career interventions, for finding a prospective area of career, where they are more likely to succeed. The model supports self-determination, which is recognized as a way of improving one’s self-esteem, a positive career identity, autonomy, maintain or improve quality of life and person’s active participation. Conclusions. Although the guidance and management of career is a challenge nowadays, it should be viewed as a positive challenge, which can nurture and develop our curiosity, flexibility, optimism, and gaining knowledge. The growing needs for labour market knowledge urge people to constantly gain competences, therefore, they can become managers of their own careers. Nevertheless, this process of career management should not be any different with people with special needs. By using advantages competence model, we rely on strengths every person has and builds on the potential of their independent career managing.

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