Motivated by concern about the ability of graduates to succeed in the workforce, universities frequently
conduct surveys of local and regional employers, to understand those companies’ expectations. These can
uncover specific needs not being addressed. Following a similar line of inquiry, prior research at Oregon
State University interviewed employers, with the aim of identifying skills of concern. The current paper
takes this research another step further by presenting a survey-based study aimed at quantifying the
prevalence and level of employers’ desire for workers who have these identified skills. Although all skills
were rated as moderately useful or better, most soft skills scored higher than most technical skills.
Nonetheless, three technical skills (source code versioning, testing and agile methods) scored
approximately as well as the soft skills; these three technical skills, like soft skills, were cross-cutting and
applicable to more than one software development context. Further survey questions revealed that
employers preferred that, to the extent that students focus on building technical skill, these learning
experiences ideally should involve creating software that students can use as evidence of their
qualifications.
A SURVEY OF EMPLOYERS’ NEEDS FOR TECHNICAL AND SOFT SKILLS AMONG NEW GRADUATESijcseit
ABSTRACT
Motivated by concern about the ability of graduates to succeed in the workforce, universities frequently conduct surveys of local and regional employers, to understand those companies’ expectations. These can uncover specific needs not being addressed. Following a similar line of inquiry, prior research at Oregon State University interviewed employers, with the aim of identifying skills of concern. The current paper takes this research another step further by presenting a survey-based study aimed at quantifying the prevalence and level of employers’ desire for workers who have these identified skills. Although all skills were rated as moderately useful or better, most soft skills scored higher than most technical skills. Nonetheless, three technical skills (source code versioning, testing and agile methods) scored approximately as well as the soft skills; these three technical skills, like soft skills, were cross-cutting and applicable to more than one software development context. Further survey questions revealed that employers preferred that, to the extent that students focus on building technical skill, these learning experiences ideally should involve creating software that students can use as evidence of their qualifications.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
Survey on adverse influencing factors in the way of successful requirement en...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Evaluation criteria for different disciplines vary from subject to subject such as Computer science,
Medicine, Management, Commerce, Art, Humanities and so on. Various evaluation criteria are being used
to measure the retention of the students’ knowledge. Some of them are in-class assignments, take home
assignments, group projects, individual projects and final examinations. When conducting lectures in
higher education institutes there can be more than one method to evaluate students to measure their
retention of knowledge. During the short period of the semester system we should be able to evaluate
students in several ways. There are practical issues when applying multiple evaluation criteria. Time
management, number of students per subject, how many lectures delivered per semester and the length of
the syllabus are the main issues.
The focus of this paper however, is on identification, finding advantages and proposing an evaluation
criterion for Computer based testing for programming subjects in higher education institutes in Sri Lanka.
Especially when evaluating hand written answers students were found to have done so many mistakes such
as programming mistakes. Therefore they lose considerable marks. Our method increases the efficiency
and the productivity of the student’s and also reduces the error rate considerably. It also has many benefits
for the lecturer. With the low number of academic members it is hard to spend more time to evaluate them
closely. A better evaluation criterion is very important for the students in this discipline because they
should stand on their own feet to survive in the industry.
This paper introduces the competency models for Operations Manager, User Interface
Designer, and Application Developers. It will serve as a guide for Information Systems students
to identify which among the three of the offered tracks would be most suited for them to pursue
according to their knowledge, skills, values and interests. The Holland’s RIASEC model and the
Values Search model of Bronwyn and Holt were utilized to determine the most dominant interest
and most dominant values of the industry computing experts. Survey assessment forms were sent
to IT Operations Manager, User Interface Designer, and Application Developer. Most dominant
values and interests of industry computing experts were determined as well as the knowledge
and skills which are mostly required by the industry in their particular area. Based on the result
of the survey, it shows that application developer and user interface designer have a closely
related values. Thus a second round of a survey would be needed to come up with the most
exclusive dominant values for the particular information systems specialization track.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
Requirement Elicitation Model (REM) in the Context of Global Software Develop...IJAAS Team
Contxext:Requirement elicitation is difficult and critical phase of requirement engineering and the case is worst in global software development (GSD). The study is about requirement elicitation in the context of GSD. Objective: Development of requirement elicitation model (REM) which can address the factors that have positive impact and the factors that have negative impact during elicitation in GSD. The propose model will give solutions and practices to the challenges during elicitation. Method: Systematic literature review (SLR) and empirical research study will be used for achieving the goals and objectives. Expected outcomes: The expected results of this study will be REM that will help vendor organizations for better elicitation during GSD.
A SURVEY OF EMPLOYERS’ NEEDS FOR TECHNICAL AND SOFT SKILLS AMONG NEW GRADUATESijcseit
ABSTRACT
Motivated by concern about the ability of graduates to succeed in the workforce, universities frequently conduct surveys of local and regional employers, to understand those companies’ expectations. These can uncover specific needs not being addressed. Following a similar line of inquiry, prior research at Oregon State University interviewed employers, with the aim of identifying skills of concern. The current paper takes this research another step further by presenting a survey-based study aimed at quantifying the prevalence and level of employers’ desire for workers who have these identified skills. Although all skills were rated as moderately useful or better, most soft skills scored higher than most technical skills. Nonetheless, three technical skills (source code versioning, testing and agile methods) scored approximately as well as the soft skills; these three technical skills, like soft skills, were cross-cutting and applicable to more than one software development context. Further survey questions revealed that employers preferred that, to the extent that students focus on building technical skill, these learning experiences ideally should involve creating software that students can use as evidence of their qualifications.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
Survey on adverse influencing factors in the way of successful requirement en...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Evaluation criteria for different disciplines vary from subject to subject such as Computer science,
Medicine, Management, Commerce, Art, Humanities and so on. Various evaluation criteria are being used
to measure the retention of the students’ knowledge. Some of them are in-class assignments, take home
assignments, group projects, individual projects and final examinations. When conducting lectures in
higher education institutes there can be more than one method to evaluate students to measure their
retention of knowledge. During the short period of the semester system we should be able to evaluate
students in several ways. There are practical issues when applying multiple evaluation criteria. Time
management, number of students per subject, how many lectures delivered per semester and the length of
the syllabus are the main issues.
The focus of this paper however, is on identification, finding advantages and proposing an evaluation
criterion for Computer based testing for programming subjects in higher education institutes in Sri Lanka.
Especially when evaluating hand written answers students were found to have done so many mistakes such
as programming mistakes. Therefore they lose considerable marks. Our method increases the efficiency
and the productivity of the student’s and also reduces the error rate considerably. It also has many benefits
for the lecturer. With the low number of academic members it is hard to spend more time to evaluate them
closely. A better evaluation criterion is very important for the students in this discipline because they
should stand on their own feet to survive in the industry.
This paper introduces the competency models for Operations Manager, User Interface
Designer, and Application Developers. It will serve as a guide for Information Systems students
to identify which among the three of the offered tracks would be most suited for them to pursue
according to their knowledge, skills, values and interests. The Holland’s RIASEC model and the
Values Search model of Bronwyn and Holt were utilized to determine the most dominant interest
and most dominant values of the industry computing experts. Survey assessment forms were sent
to IT Operations Manager, User Interface Designer, and Application Developer. Most dominant
values and interests of industry computing experts were determined as well as the knowledge
and skills which are mostly required by the industry in their particular area. Based on the result
of the survey, it shows that application developer and user interface designer have a closely
related values. Thus a second round of a survey would be needed to come up with the most
exclusive dominant values for the particular information systems specialization track.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on each other, discretization data and processing them was performed by GeNIe. The proper course would be advised for students to continue their education.
Requirement Elicitation Model (REM) in the Context of Global Software Develop...IJAAS Team
Contxext:Requirement elicitation is difficult and critical phase of requirement engineering and the case is worst in global software development (GSD). The study is about requirement elicitation in the context of GSD. Objective: Development of requirement elicitation model (REM) which can address the factors that have positive impact and the factors that have negative impact during elicitation in GSD. The propose model will give solutions and practices to the challenges during elicitation. Method: Systematic literature review (SLR) and empirical research study will be used for achieving the goals and objectives. Expected outcomes: The expected results of this study will be REM that will help vendor organizations for better elicitation during GSD.
CRESUS: A TOOL TO SUPPORT COLLABORATIVE REQUIREMENTS ELICITATION THROUGH ENHA...cscpconf
Communicating an organisation's requirements in a semantically consistent and understandable manner and then reflecting the potential impact of those requirements on the IT infrastructure presents a major challenge among stakeholders. Initial research findings indicate a desire among business executives for a tool that allows them to communicate organisational changes using natural language and a simulation of the IT infrastructure that supports those changes. Building on a detailed analysis and evaluation of these findings, the innovative CRESUS tool was designed and implemented. The purpose of this research was to investigate to what extent CRESUS both aids communication in the development of a shared understanding and supports collaborative requirements elicitation to bring about organisational, and associated IT infrastructural, change. This paper presents promising results that show how such a tool can facilitate collaborative requirements elicitation through increased communication around organisational change and the IT infrastructure.
Opinion Mining Techniques for Non-English Languages: An OverviewCSCJournals
The amount of user-generated data on web is increasing day by day giving rise to necessity of automatic tools to analyze huge data and extract useful information from it. Opinion Mining is an emerging area of research concerning with extracting and analyzing opinions expressed in texts. It is a language and domain dependent task having number of applications like recommender systems, review analysis, marketing systems, etc. Early research in the field of opinion mining has concentrated on English language. Many opinion mining tools and linguistic resources have been built for English language. Availability of information in regional languages has motivated researchers to develop tools and resources for non-English languages. In this paper we present a survey on the opinion mining research for non-English languages.
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
Analyzing the solutions of DEA through information visualization and data min...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/analyzing-the-solutions-of-dea-through-information-visualization-and-data-mining-techniques-smartdea-framework/
Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the pro-posed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.
LINKING SOFTWARE DEVELOPMENT PHASE AND PRODUCT ATTRIBUTES WITH USER EVALUATIO...csandit
This paper presents an evaluation methodology to reveal the relationships between the
attributes of software products, practices applied during the development phase and the user
evaluation of the products. For the case study, the games sector has been chosen due to easy
access to the user evaluation of this type of software products. Product attributes and practices
applied during the development phase have been collected from the developers via
questionnaires. User evaluation results were collected from a group of independent evaluators.
Two bipartite networks were created using the gathered data. The first network maps software
products to the practices applied during the development phase and the second network maps
the products to the product attributes. According to the links, similarities were determined and
subgroups of products were obtained according to selected development phase practices. By
this way, the effect of development phase on the user evaluation has been investigated.
Multiagent Systems are autonomous intelligent systems. In many academic institutions student
admissions are performed after generating merit lists. Generation of merit lists is preceded by
manual scrutiny of admission forms. This manual scrutiny is a knowledge-intensive, tedious and
error-prone task. In this paper the design, implementation and testing of Multiagent System for
Scrutiny of Admission Forms (MASAF) using Automatic Knowledge Capture is presented.
MASAF consists of three agents namely: Form agent, Record agent, and Scrutiny agent. These
three agents, using ontology, cooperatively fulfill the goal of highlighting the discrepancies
in filled forms. MASAF has been tested by scrutinizing about 1000 forms and all of
discrepancies found were correct as verified by human scrutinizer. Thus it can be concluded
that using Multiagent system for scrutiny of forms can reduce human intervention, improve
performance in terms of speed and accuracy. The system can be enhanced to automatically
correct the discrepancies in forms.
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
INVESTIGATION OF ATTITUDES TOWARDS COMPUTER PROGRAMMING IN TERMS OF VARIOUS V...ijpla
This study aims to determine the attitudes of individuals towards computer programming in terms of
various variables. The study group consists of the students of Kastamonu University Department of
Computer Education and Instructional Technologies Teaching (CEIT), Department of Computer
Engineering, and Department of Computer Programming. Data were collected via Attitude towards
Computer Programming Scale (AtCPS).The results of this study show that students have neutral attitudes
towards computer programming in general. Male computer programming students have significantly
higher attitudes towards programming in comparison to female computer programming students. In
addition, attitude towards computer programming statistically varies by grade. The higher is grade, the
lower is attitude. The more time CEIT and computer programming students spend on computer for
programming purposes daily, the more positive attitudes they have towards programming. Attitude
significantly varies by graduated high school only among CEIT students.
Investigation of Attitudes Towards Computer Programming in Terms of Various V...ijpla
This study aims to determine the attitudes of individuals towards computer programming in terms of
various variables. The study group consists of the students of Kastamonu University Department of
Computer Education and Instructional Technologies Teaching (CEIT), Department of Computer
Engineering, and Department of Computer Programming. Data were collected via Attitude towards
Computer Programming Scale (AtCPS).The results of this study show that students have neutral attitudes
towards computer programming in general. Male computer programming students have significantly
higher attitudes towards programming in comparison to female computer programming students. In
addition, attitude towards computer programming statistically varies by grade. The higher is grade, the
lower is attitude. The more time CEIT and computer programming students spend on computer for
programming purposes daily, the more positive attitudes they have towards programming. Attitude
significantly varies by graduated high school only among CEIT students.
MULTILEVEL ANALYSIS OF FACTORS PREDICTING SELF EFFICACY IN COMPUTER PROGRAMMINGIJITE
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender,
computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South –
West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254
computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely,
Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model
(LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and
SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical
backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM
showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors.
The institution – level variable had large predictive value on programming self efficacy. Computer science
departments should increase the number of mathematics courses in their curriculum.
Multilevel Analysis of Factors Predicting Self Efficacy in Computer ProgrammingIJITE
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender, computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South – West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254 computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely, Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model (LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors. The institution – level variable had large predictive value on programming self efficacy. Computer science departments should increase the number of mathematics courses in their curriculum.
MULTILEVEL ANALYSIS OF FACTORS PREDICTING SELF EFFICACY IN COMPUTER PROGRAMMING IJITE
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender,
computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South –
West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254
computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely,
Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model
(LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and
SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical
backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM
showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors.
The institution – level variable had large predictive value on programming self efficacy. Computer science
departments should increase the number of mathematics courses in their curriculum.
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender,
computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South –
West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254
computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely,
Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model
(LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and
SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical
backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM
showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors.
The institution – level variable had large predictive value on programming self efficacy. Computer science
departments should increase the number of mathematics courses in their curriculum.
CRESUS: A TOOL TO SUPPORT COLLABORATIVE REQUIREMENTS ELICITATION THROUGH ENHA...cscpconf
Communicating an organisation's requirements in a semantically consistent and understandable manner and then reflecting the potential impact of those requirements on the IT infrastructure presents a major challenge among stakeholders. Initial research findings indicate a desire among business executives for a tool that allows them to communicate organisational changes using natural language and a simulation of the IT infrastructure that supports those changes. Building on a detailed analysis and evaluation of these findings, the innovative CRESUS tool was designed and implemented. The purpose of this research was to investigate to what extent CRESUS both aids communication in the development of a shared understanding and supports collaborative requirements elicitation to bring about organisational, and associated IT infrastructural, change. This paper presents promising results that show how such a tool can facilitate collaborative requirements elicitation through increased communication around organisational change and the IT infrastructure.
Opinion Mining Techniques for Non-English Languages: An OverviewCSCJournals
The amount of user-generated data on web is increasing day by day giving rise to necessity of automatic tools to analyze huge data and extract useful information from it. Opinion Mining is an emerging area of research concerning with extracting and analyzing opinions expressed in texts. It is a language and domain dependent task having number of applications like recommender systems, review analysis, marketing systems, etc. Early research in the field of opinion mining has concentrated on English language. Many opinion mining tools and linguistic resources have been built for English language. Availability of information in regional languages has motivated researchers to develop tools and resources for non-English languages. In this paper we present a survey on the opinion mining research for non-English languages.
The Architecture of System for Predicting Student Performance based on the Da...Thada Jantakoon
The goals of this study are to develop the architecture of a system for predicting student performance based on data science approaches (SPPS-DSA Architecture) and evaluate the SPPS-DSA Architecture. The research process is divided into two stages: (1) context analysis and (2) development and assessment. The data is analyzed by means of standardized deviations statistically. The research findings suggested that the SPPS-DSA architecture, according to the research findings, consists of three key components: (i) data source, (ii) machine learning methods and attributes, and (iii) data science process. The SPPS-DSA architecture is rated as the highest appropriate overall. Predicting student performance helps educators and students improve their teaching and learning processes. Predicting student performance using various analytical methods is reviewed here. Most researchers used CGPA and internal assessment as data sets. In terms of prediction methods, classification is widely used in educational data science. Researchers most commonly used neural networks and decision trees to predict student performance under classification techniques.
Analyzing the solutions of DEA through information visualization and data min...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/analyzing-the-solutions-of-dea-through-information-visualization-and-data-mining-techniques-smartdea-framework/
Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the pro-posed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.
LINKING SOFTWARE DEVELOPMENT PHASE AND PRODUCT ATTRIBUTES WITH USER EVALUATIO...csandit
This paper presents an evaluation methodology to reveal the relationships between the
attributes of software products, practices applied during the development phase and the user
evaluation of the products. For the case study, the games sector has been chosen due to easy
access to the user evaluation of this type of software products. Product attributes and practices
applied during the development phase have been collected from the developers via
questionnaires. User evaluation results were collected from a group of independent evaluators.
Two bipartite networks were created using the gathered data. The first network maps software
products to the practices applied during the development phase and the second network maps
the products to the product attributes. According to the links, similarities were determined and
subgroups of products were obtained according to selected development phase practices. By
this way, the effect of development phase on the user evaluation has been investigated.
Multiagent Systems are autonomous intelligent systems. In many academic institutions student
admissions are performed after generating merit lists. Generation of merit lists is preceded by
manual scrutiny of admission forms. This manual scrutiny is a knowledge-intensive, tedious and
error-prone task. In this paper the design, implementation and testing of Multiagent System for
Scrutiny of Admission Forms (MASAF) using Automatic Knowledge Capture is presented.
MASAF consists of three agents namely: Form agent, Record agent, and Scrutiny agent. These
three agents, using ontology, cooperatively fulfill the goal of highlighting the discrepancies
in filled forms. MASAF has been tested by scrutinizing about 1000 forms and all of
discrepancies found were correct as verified by human scrutinizer. Thus it can be concluded
that using Multiagent system for scrutiny of forms can reduce human intervention, improve
performance in terms of speed and accuracy. The system can be enhanced to automatically
correct the discrepancies in forms.
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
EMPLOYERS’ NEEDS FOR COMPUTER SCIENCE, INFORMATION TECHNOLOGY AND SOFTWARE EN...ijcseit
Graduates of computer science programs often lack skills that employers desire among software
developers. These include, for example, weaknesses in the areas of collaboration, communication, and
software testing. Further research can help to refine this list by providing insight into additional skills that
are of rising or regional importance. This paper therefore presents a study aimed at uncovering desirable
technical and soft skills for graduates of computer science in the Pacific Northwest region of the United
States. Interviews of 11 employers, including both managers and recruiters, highlighted the prominent
importance of skills related to web development, relational databases, and testing. Additionally, it
spotlighted not only widely-recognized soft skills such as those related to collaboration and
communication, but additionally on skills tied to personal attributes such as innovating, coping with
ambiguity and learning quickly. The results provide insights for what skills and personal attributes to
include in a future survey of employers aimed at quantifying the importance of skills on this list.
INVESTIGATION OF ATTITUDES TOWARDS COMPUTER PROGRAMMING IN TERMS OF VARIOUS V...ijpla
This study aims to determine the attitudes of individuals towards computer programming in terms of
various variables. The study group consists of the students of Kastamonu University Department of
Computer Education and Instructional Technologies Teaching (CEIT), Department of Computer
Engineering, and Department of Computer Programming. Data were collected via Attitude towards
Computer Programming Scale (AtCPS).The results of this study show that students have neutral attitudes
towards computer programming in general. Male computer programming students have significantly
higher attitudes towards programming in comparison to female computer programming students. In
addition, attitude towards computer programming statistically varies by grade. The higher is grade, the
lower is attitude. The more time CEIT and computer programming students spend on computer for
programming purposes daily, the more positive attitudes they have towards programming. Attitude
significantly varies by graduated high school only among CEIT students.
Investigation of Attitudes Towards Computer Programming in Terms of Various V...ijpla
This study aims to determine the attitudes of individuals towards computer programming in terms of
various variables. The study group consists of the students of Kastamonu University Department of
Computer Education and Instructional Technologies Teaching (CEIT), Department of Computer
Engineering, and Department of Computer Programming. Data were collected via Attitude towards
Computer Programming Scale (AtCPS).The results of this study show that students have neutral attitudes
towards computer programming in general. Male computer programming students have significantly
higher attitudes towards programming in comparison to female computer programming students. In
addition, attitude towards computer programming statistically varies by grade. The higher is grade, the
lower is attitude. The more time CEIT and computer programming students spend on computer for
programming purposes daily, the more positive attitudes they have towards programming. Attitude
significantly varies by graduated high school only among CEIT students.
MULTILEVEL ANALYSIS OF FACTORS PREDICTING SELF EFFICACY IN COMPUTER PROGRAMMINGIJITE
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender,
computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South –
West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254
computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely,
Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model
(LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and
SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical
backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM
showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors.
The institution – level variable had large predictive value on programming self efficacy. Computer science
departments should increase the number of mathematics courses in their curriculum.
Multilevel Analysis of Factors Predicting Self Efficacy in Computer ProgrammingIJITE
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender, computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South – West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254 computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely, Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model (LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors. The institution – level variable had large predictive value on programming self efficacy. Computer science departments should increase the number of mathematics courses in their curriculum.
MULTILEVEL ANALYSIS OF FACTORS PREDICTING SELF EFFICACY IN COMPUTER PROGRAMMING IJITE
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender,
computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South –
West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254
computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely,
Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model
(LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and
SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical
backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM
showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors.
The institution – level variable had large predictive value on programming self efficacy. Computer science
departments should increase the number of mathematics courses in their curriculum.
This study used multilevel analysis to determine the predictive value of selected intrinsic factors (gender,
computer ownership, mathematics background and computer experience) and institutional type (an
extrinsic factor) on undergraduates’ Self Efficacy in Java Computer Programming (SEiJCP) in South –
West, Nigeria. The study adopted a correlational design. Purposive Sampling was used to select 254
computer science undergraduates from four universities (three federal-owned and one state-owned) in
south-west, Nigeria. Three research questions were answered. Two research instruments namely,
Computer experience scale (r = 0.84) and Java Programming Self Efficacy Scale (JPSES, r = 0.96) were
used to collect data. Data were analysed using descriptive statistics, and null and linear growth model
(LGM) procedures. The intercorrelation coefficients among the extrinsic factor, intrinsic factors and
SEiJCP were moderate. Null model shows that the variations in SEiJCP accounted for by insitutional level
differences was 99.0%. The fixed part of the LGM of intrinsic factors showed that only mathematical
backgroung contributed significantly (p < 0.05) to the prediction of SEiJCP. The random part of the LGM
showed no significant contributions of the interactions of the intrinsic factors, to the prediction of SEiJCP.
About 60.0% of the student level variation in SEiJCP is explained by the differences in intrinsic factors.
The institution – level variable had large predictive value on programming self efficacy. Computer science
departments should increase the number of mathematics courses in their curriculum.
DATA MINING FOR STUDENTS’ EMPLOYABILITY PREDICTIONCSEIJJournal
This study has been undertaken to predict the student employability.Assessing student employability
provides a method of integrating student abilities and employer business requirements, which is becoming
an increasingly important concern for academic institutions. Improving student evaluation techniques for
employability can help students to have a better understanding of business organizations and find the right
one for them. The data for the training classification models is gathered through a survey in which students
are asked to fill out a questionnaire in which they may indicate their abilities and academic achievement.
This information may be used to determine their competency in a variety of skill categories, including soft
skills, problem-solving skills and technical abilities and so on.The goal of this research is to use data
mining to predict student employability by considering different factors such as skills that the students have
gained during their diploma level and time duration with respect to the knowledge they have captured
when they expect the placement at the end of graduation. Further during this research most specific skills
with relevant to each job category also was identified. In this research for the prediction of the student
employability different data mining models such as such as KNN, Naive Bayer’s, and Decision Tree were
evaluated and out of that best model also was identified for this institute's student’s employability
prediction.So, in this research classification and association techniques were used and evaluated.
Requirement engineering is a key ingredient for software development to be effective. Apart from the
traditional software requirement which is not much appropriate for new emerging software such as smart
handheld device based software. In many perspectives of requirement engineering, traditional and new
emerging software are not similar. Whereas requirement engineering of traditional software needs more
research, it is obvious that new emerging software needs methodically and in-depth research for improved
productivity, quality, risk management and validity. In particular, the result of this paper shows that how
effective requirement engineering can improve in project negotiation, project planning, managing feature
creep, testing, defect, rework and product quality. This paper also shows a new methodology which is
focused on users work process applicable for eliciting the requirement of traditional software and any new
type software of smart handheld device such as iPad. As an example, the paper shows how the methodology
will be applied as a software requirement of iPad-based software for play-group students.
Investigating the roles of demographic profiles on usability assessment: case...IAESIJAI
Postgraduate education is the highest education structure in all countries across global. In Malaysia, postgraduate education is usually pursued by several candidates, such as academicians, professionals, and motivated junior learners. As such, one of the requirements of postgraduate education is to have a publication as a mandatory graduation requirement. Thus, one of the challenging issues in the publication is to ensure the quality of the references and citation. However, the lack of mobile applications available that focus on citation management caused several problems such as succumbing to predatory journals, poor citation work, desk rejection, and
inaccurate facts. Therefore, the purpose of this study is twofold: first, to investigate the roles and usability assessment of CiteGuru application as a mobile solution for improving learners’ skills, ability, and knowledge on referencing and second, to investigate the roles of demographic profiles on the usability perception among the respondents. A quantitative research methodology using a survey was adopted with 23 expert panels selected based on three distinct positions–academic, industries, and librarians. Data were analysed using statistical package for social science version 26. The result indicates that i) the panel rate the usability of the application as
acceptable and ii) demographic profiles (sector, education, and gender) prove insignificant on the usability assessment.
Software Defect Prediction Using Local and Global AnalysisEditor IJMTER
The software defect factors are used to measure the quality of the software. The software
effort estimation is used to measure the effort required for the software development process. The defect
factor makes an impact on the software development effort. Software development and cost factors are
also decided with reference to the defect and effort factors. The software defects are predicted with
reference to the module information. Module link information are used in the effort estimation process.
Data mining techniques are used in the software analysis process. Clustering techniques are used
in the property grouping process. Rule mining methods are used to learn rules from clustered data
values. The “WHERE” clustering scheme and “WHICH” rule mining scheme are used in the defect
prediction and effort estimation process. The system uses the module information for the defect
prediction and effort estimation process.
The proposed system is designed to improve the defect prediction and effort estimation process.
The Single Objective Genetic Algorithm (SOGA) is used in the clustering process. The rule learning
operations are carried out sing the Apriori algorithm. The system improves the cluster accuracy levels.
The defect prediction and effort estimation accuracy is also improved by the system. The system is
developed using the Java language and Oracle relation database environment.
The recruitment of new personnel is one of the most essential business processes which affect the quality of
human capital within any company. It is highly essential for the companies to ensure the recruitment of
right talent to maintain a competitive edge over the others in the market. However IT companies often face
a problem while recruiting new people for their ongoing projects due to lack of a proper framework that
defines a criteria for the selection process. In this paper we aim to develop a framework that would allow
any project manager to take the right decision for selecting new talent by correlating performance
parameters with the other domain-specific attributes of the candidates. Also, another important motivation
behind this project is to check the validity of the selection procedure often followed by various big
companies in both public and private sectors which focus only on academic scores, GPA/grades of students
from colleges and other academic backgrounds. We test if such a decision will produce optimal results in
the industry or is there a need for change that offers a more holistic approach to recruitment of new talent
in the software companies. The scope of this work extends beyond the IT domain and a similar procedure
can be adopted to develop a recruitment framework in other fields as well. Data-mining techniques provide
useful information from the historical projects depending on which the hiring-manager can make decisions
for recruiting high-quality workforce. This study aims to bridge this hiatus by developing a data-mining
framework based on an ensemble-learning technique to refocus on the criteria for personnel selection. The
results from this research clearly demonstrated that there is a need to refocus on the selection-criteria for
quality objectives.
Clustering Students of Computer in Terms of Level of ProgrammingEditor IJCATR
Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains.
In existing system the faculty performance has calculated on the basis of two parameters i.e. Student feedback and the result of student in that subject. In existing system we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining
Techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification.
But in proposed system, I will analyse the faculty performance using 4 parameters i.e., student complaint about faculty, Student review feedback for faculty, students feedback, and students result etc.
For this proposed system I will be going to use opinion mining technique for analyzing performance of faculty and calculating score of each faculty.
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A SURVEY OF EMPLOYERS’ NEEDS FOR TECHNICAL AND SOFT SKILLS AMONG NEW GRADUATES
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.8, No.5/6, December 2018
DOI : 10.5121/ijcseit.2018.8602 11
A SURVEY OF EMPLOYERS’ NEEDS FOR
TECHNICAL AND SOFT SKILLS AMONG NEW
GRADUATES
Christopher Scaffidi
School of Electrical Engineering and Computer Science,
Oregon State University, US
ABSTRACT
Motivated by concern about the ability of graduates to succeed in the workforce, universities frequently
conduct surveys of local and regional employers, to understand those companies’ expectations. These can
uncover specific needs not being addressed. Following a similar line of inquiry, prior research at Oregon
State University interviewed employers, with the aim of identifying skills of concern. The current paper
takes this research another step further by presenting a survey-based study aimed at quantifying the
prevalence and level of employers’ desire for workers who have these identified skills. Although all skills
were rated as moderately useful or better, most soft skills scored higher than most technical skills.
Nonetheless, three technical skills (source code versioning, testing and agile methods) scored
approximately as well as the soft skills; these three technical skills, like soft skills, were cross-cutting and
applicable to more than one software development context. Further survey questions revealed that
employers preferred that, to the extent that students focus on building technical skill, these learning
experiences ideally should involve creating software that students can use as evidence of their
qualifications.
KEYWORDS
Computer science education, Software engineering education, Information technology education
1. INTRODUCTION
1.1. BACKGROUND
Software Developers Recently Graduated From College Frequently Need To Learn And Improve
A Range Of Skills, Even After Obtaining Their First Jobs [1, 2, 3, 4]. While Some Gaps In
Expertise Relate To Soft Skills And Associated Personal Attributes, Such As Those Related To
Communication And Collaboration [1, 4], While Others Relate To Technical Skills Such As
Source Code Control, Testing, And Specific Programming Languages [1, 4, 5]. Such Limitations
In Recent Graduates’ Abilities Can Hamper Their Comfort And Productivity [6].
Motivated By Concern About The Ability Of Graduates To Succeed In The Workforce,
Universities Frequently Conduct Surveys Of Local And Regional Employers, To Understand
Those Companies’ Expectations. These Can Uncover Specific Needs Not Being Addressed. For
Example, Research In The United Kingdom Found That Web-Based Programming Skills Were
Of High Importance [7], Research With North Dakota State University’s Employer Contacts
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.8, No.5/6, December 2018
12
Revealed The Need For Students To Do More Realistic Projects Before They Graduated [8],
Research In Australia Highlighted The Need For More Business-Related Knowledge [9]. The
Mismatch Between Identified Skills And University Curricula Has Aided In Identifying
Opportunities For Improving Curricula By Creating, Expanding Or Reordering Courses [9, 10].
Following a similar line of inquiry, prior research at Oregon State University interviewed
employers, with the aim of identifying skills of concern [11]. The skills revealed by these
interviews covered both technical and soft skills (Table 1) and echoed many highlighted in the
studies cited above. These included, for example, agile methods, the ability to collaborate, user
interface design, and entrepreneurship skills.
Technical/domain skills Soft skills / attributes
Agile methods Ability to collaborate
Mobile development Ability to cope with ambiguity
Non-relational data/big data Learning and curiosity
Relational databases Passionate innovation
Source code management
System administration
Testing and quality assurance
User interface design
Web development
Business & entrepreneurship
Table 1: Skills and attributes identified as important by employers in prior interviews [11];
business skills are listed below as domain-specific yet non-technical from the standpoint of CS
1.2. STUDY OBJECTIVES
The current paper takes this research another step further by presenting a survey-based study
aimed at quantifying the prevalence and level of employers’ desire for workers who have the
skills listed in Table 1.
Three research questions framed the study:
• RQ1: What preferences do employers have about new hires’ educational attainment in
computer science?
• RQ2: What skills do employers value most highly among future hires from computer
science?
• RQ3: What forms of evidence do employers find most useful for evaluating students and
graduates?
1.3. ORGANIZATION
Section 2 presents materials and methods, while Section 3 summarizes results. Section 4
discusses key findings from the data, which Section 5 juxtaposes with related work. Finally,
Section 6 summarizes conclusions as well as focal points for future work aimed at enhancing our
university curriculum in order to better meet employers’ hiring needs.
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.8, No.5/6, December 2018
13
2. MATERIALS AND METHODS
2.1. SURVEY SCOPE
A survey was deployed to obtain data for answering the three research questions (Section 1).
Participants were recruited with emails sent to a list of employers and former graduates who had
previously indicated a desire to participate in research regarding software engineering education
and corporate hiring. The email contained a link to the survey, deployed on the Qualtrics
platform.
The survey contained 29 questions organized into 3 sections. The first invited participants to rate
their likelihood of hiring students and graduates in computer science with specific educational
attainment (e.g., Master’s degree). The second section asked participants to rate the usefulness of
technical and soft skills (e.g., SQL/database skills and writing skills); these were selected directly
from the list of skills mentioned by employers in the earlier interviews [11], with the exception of
“ability to communicate well” which was separated into two skills for this survey as “ability to
communicate verbally” and “ability to communicate in writing.” The third section had
participants rate how useful they found different kinds of items for evaluating applicants who had
a background in computer science (e.g., resume, transcript, source code). Throughout all three
sections, each question presented a 5-level Likert scale.
2.2. ANALYSIS
Data for each question were summarized two different ways. First, the proportion of non-missing
answers at the two highest levels the Likert scale was computed (i.e., the proportion of responses
that essentially agreed with the item presented). Second, the average of non-missing answers was
computed to generate an average rating for each question.
These per-question statistics were then compared in order to answer the research questions. For
example, to answer RQ2, ratings for different skills were compared with one another (e.g.,
SQL/database versus writing skills, versus the other skills considered). Formal statistical tests
were not conducted, due to the high risk of Type I error (there were 16 different skills, for
example, posing difficulties in comparing all of them to one another) as well as Type II error (as,
with 26 respondents, statistical power was low). Instead, the two summary statistics mentioned
above were sorted and plotted on a bar chart in order to reveal unmistakable visual trends. These
trends could be used to generate statistical hypotheses for testing in future research. The
Discussion (Section 4) compares results to those from the interviews that motivated the survey,
helping to triangulate and confirm conclusions.
3. RESULTS
3.1. WHAT PREFERENCES DO EMPLOYERS HAVE ABOUT NEW HIRES’ EDUCATIONAL
ATTAINMENT IN COMPUTER SCIENCE?
The survey asked respondents to rate how likely their companies were to hire students from six
different categories that varied by educational attainment. These ranged from hiring Bachelor’s
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students as interns prior to graduation all the way to graduates who had obtained a PhD. The
responses showed two clear trends.
First, for each of the three degrees, the likelihood of hiring graduates exceeded the likelihood of
hiring students for internships prior to graduation. These trends appeared in both the proportions
reporting they were likely to hire students of each category (Fig. 1) and the average rating of
likelihood (Fig. 2).
Second, the likelihood of hiring students generally decreased with rising level of educational
attainment. Respondents were relatively unlikely to hire PhD students (either before or during
graduation), relatively more likely to hire Master’s students, and the most likely to hire
Bachelor’s students. At the low end, only 21% of respondents were likely or definitely intending
to hire PhD students for internships. At the high end, 88% indicated the same for students who
had graduated with a Bachelor’s degree.
Fig. 1: Proportion of respondents who were likely or definitely going to hire students
in the next 5 years, by degree (Bachelor’s, Master’s, or PhD) and
by graduation status (after graduation, or as an intern before graduation).
3.2. WHAT SKILLS DO EMPLOYERS VALUE MOST HIGHLY AMONG FUTURE HIRES FROM
COMPUTER SCIENCE?
Fig. 2: Average estimated likelihood of hiring students in the next 5 years
0.00 1.00 2.00 3.00 4.00
BS Graduates
BS Internship
MS Graduates
MS Internship
PhD Graduates
PhD Internship
Definitely Unlikely 50-50 Likely Definitely
not chance
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Respondents rated the usefulness of 7 soft skills and attributes as well as 10 technical skills.
Plotting the proportion rating each skill as very or extremely useful (Fig. 3), and plotting the
average rating of each skill (Fig. 4), revealed three trends.
First, all skills were rated on average at least as moderately useful. The lowest-rated was the
business/entrepreneurship soft skill, which only 50% considered very or extremely useful. At the
high end, 100% of respondents considered a hire’s drive to learn as very or extremely useful. This
result suggests that the collection of skills for the survey, based on prior interviews [11], was
well-chosen.
Fig. 3: Proportion of respondents who indicated that specific skills and attributes were
very useful or extremely useful.
Second, almost all soft skills/attributes were more highly valued than almost all technical skills.
All but one of the soft skills/attributes were rated as very or extremely useful by at least 80% of
respondents, the average rating across all 7 soft skills was 3.44 (between very and extremely
useful). In contrast, only half of the technical skills were rated as very or extremely useful (source
code versioning, testing, agile methods, SQL/database, and web development). The average
rating of among all technical skills was only 2.95 (slightly below very useful).
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Fig. 4: Average ratings of the usefulness of soft skills/attributes and technical skills.
Third, all 3 of the top-rated technical skills were, in some sense, cross-cutting and applicable to
more than one software development context. These 3 skills were source code versioning, testing,
and agile methods. All of these can be applied in the context of web development, mobile app
development, embedded development, and many other contexts (e.g., desktop software
development, cloud development, etc.) not specifically examined in this survey. The result may
imply that generalizable technical skills are viewed as more useful than skills targeted to specific
development contexts.
3.3. WHAT FORMS OF EVIDENCE DO EMPLOYERS FIND MOST USEFUL FOR EVALUATING
STUDENTS AND GRADUATES?
Respondents rated the usefulness of 6 items that job applicants may present as evidence of their
qualifications related to computer science. The results revealed two trends (Figs. 5, 6).
First, school-centric items were generally considered of little or no usefulness. Transcripts were
rated as only slightly more than moderately useful, and only 25% of respondents considered
transcripts to be very or extremely useful. Even references—which, for a student or recent
graduate, might generally just include professors—fared only slightly better, at 40% of
respondents and a still only slightly-above-moderate level of usefulness. The resume scored
higher, nearly attaining a rating of “very useful.”
Second, items related to making software were generally considered of high usefulness. With a
rating of very or extremely useful by 92% of respondents, the highest-rated was source code,
which to be precise, the survey described as “Source code (e.g., via github) that the applicant
wrote.” The next highest was “Software (e.g., apps, websites) that the applicant made,” which
was rated as very or extremely useful by 88% of respondents. The only software-centric item that
obtained a low score was “Screenshots, videos of applicant’s past work,” which obtained ratings
between transcripts and references.
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Fig. 5: Proportion of respondents that considered each item to be very useful or extremely useful as
evidence of applicants’ qualifications.
Fig. 6: Average ratings of each item as evidence of an applicant’s qualifications.
4. DISCUSSION
The results address the research questions that motivated this work.
RQ1: What preferences do employers have about new hires’ educational attainment in computer
science? On the whole, respondents showed a consistent higher preference for graduates with
relatively lower degree status (Bachelor’s, Master’s, PhD) and for graduates over interns.
RQ2: What skills do employers value most highly among future hires from computer science?
Although all skills were rated as moderately useful or better, most soft skills scored higher than
most technical skills. Nonetheless, 3 technical skills scored approximately as well as the soft
skills (source code versioning, testing and agile methods), all which—like soft skills—were
cross-cutting and applicable to more than one software development context.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Source code
Working software
Resume
References
Screenshots/video
Transcript
0.00 1.00 2.00 3.00 4.00
Source code
Working software
Resume
References
Screenshots/video
Transcript
Not useful Slightly Moderately Very Extremely
at all useful useful useful useful
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RQ3: What forms of evidence do employers find most useful for evaluating students and
graduates? School-based evidence generally obtained low scores, and evidence related to actual
project work generally obtained the highest.
The results for the first two research questions suggest employers may be most interested in
finding entry-level employees with strong aptitude and desire to learn, whom they can train and
develop in a specific technical direction pertinent to the company’s needs. This inference
dovetails with results from the prior interviews of employers [11], almost all of whom indicated
wanting workers with a drive to learn. They mentioned phrases like “lifelong learners,”
“curiosity,” and “drive.” Some interviewees also explicitly said that applicants’ specific language
and platform experience mattered relatively little because it was relatively easy for developers
experienced with one to pick up another.
Survey and interview respondents both very strongly emphasized the need for soft skills. One
interviewee highlighted the relative importance of soft skills over technical skills by stating, “We
do not value people sitting at their desk and writing code all day.” In particular, all but one
interviewee emphasized the importance of communication skills; the survey results provide more
fine-grained insight into what this means, in that verbal and writing communication skills were
both rated as highly important.
The results for the third research question are striking in terms of highlighting the emphasis that
employers place on whether students and recent graduates can demonstrate experience with actual
software development tasks. Transcripts, references and screenshots do not necessarily provide
such insight: course grades might primarily be based on exam scores, faculty who serve as
references might primarily emphasize such grades rather than project work, and screenshots offer
only a static impression of software and little insight into the process of its creation. In contrast, a
well-written resume can discuss how software was built, a working app or website can serve as
the focus of interview questions about the software’s creation, and source code reveals the
internals of the software’s construction.
The emphasis on real experience with the process of creating working software also manifested in
interview results [11]. Several interviewees emphasized the importance of using tools and design
patterns in academic training that matched those used in industry, and three specifically noted the
importance for applicants to tell stories about past projects in ways that demonstrated knowledge
and expertise. One of them explicitly said they only wanted workers capable of “thinking beyond
homework assignments,” emphasizing the potential disconnect between academic work and real-
world practicable skills.
5. RELATED WORK
Numerous studies have reported that recent graduates lack certain skills that they must master
while on the job (e.g., [1, 2, 3, 4]). For example, based on a literature survey of empirical studies,
Radermacher and Walia determined that the most-often mentioned gap in new workers’ skills
existed in the area of written communication [1]. Other oft-cited weaknesses included oral
communication, project management and teamwork. All these are soft skills, consistent with the
results of the current survey, which revealed the importance of training students in soft skills.
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Moreover, Radermacher and Walia noted several lacking technical skills, including in the areas of
software tools, testing and programming. These skills are cross-cutting, again consistent with the
results of the current survey. Unlike the current survey, Radermacher and Walia did not mention
the importance of source code control or specific programming languages, although those have
appeared in lists of necessary skills generated by other studies (e.g., [4, 5]).
Unlike the studies cited above, the current survey also investigated what forms of evidence about
skills are sought by employers. This has been a topic of concern in a handful of other studies
(e.g., [12, 13, 14]). For example, Marlow and Dabbish interviewed members of the GitHub
“social coding” community to investigate the extent to which accounts on that site facilitated
hiring and recruitment of software developers [12]. They found that potential employers tended to
focus on how frequently and consistently programmers committed work to online project
repositories, as well as how well networked programmers were (e.g., through having followers,
having submitted pull requests, and having interacted with others on the site). In fact,
interviewees stated that they relied upon these signals when judging candidates’ skills even more
strongly than they relied upon information written in resumes – consistent with findings from the
current survey. Likewise, a survey of employers found that they do value open source projects
reported on a resume, as long as the development practices reflected in those projects are
compatible with the processes used at each employer [14].
6. CONCLUSIONS AND FUTURE WORK
6.1. CONCLUSIONS
In short, this study has provided additional insight into what it means to train work-ready
graduates, with an emphasis noted above on development of soft skills and manifestation of
technical skills in actual source code and working software.
The findings of prior studies (Section 5) are consistent with those of the current study, which also
adds to the literature by providing quantitative evidence about the relative importance of different
evidence signals. In particular, as shown in Figures 5 and 6, the current survey revealed a clear
progression of importance: evidence from the abstract academic setting (e.g., transcripts) was
least persuasive, descriptive evidence of prior work (e.g., references and resume) was moderately
persuasive, and direct concrete materials derived from project work (e.g., working software and
source code) was most persuasive.
Taken together, all the results from the survey and the interviews suggest that training work-ready
graduates involves cultivating a drive to learn and other soft skills/attributes at least as much as it
involves giving students technical skills. Advanced degrees, such as the Master’s and PhD, in
particular, are at risk of providing little additional value unless if they reinforce these skills and
attributes. And, to the extent that students at all levels build technical skill, they should do so in a
way that manifests expertise through practical experiences creating software that they can use as
evidence of their qualifications.
6.2. SCOPE OF POTENTIAL FUTURE WORK
Future work could build on this research in several areas.
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First, a larger survey involving more employers could address the threats to validity entailed by
the small sample size of the current study. The sample size prevented use of statistical tests to
confirm trends observed in the data, so the findings above, while visually salient, might not
actually be significant. Moreover, obtaining demographic data about respondents (e.g., company
size and target markets) could aid in ascertaining the extent to which any sampling and/or non-
response bias limits generalizability. Additional questions that go beyond opinions and ask about
historical facts (e.g., asking about students hired by the company in the past) could aid in further
triangulation and establishing aspects of internal validity. The results of this survey provide
insights for interesting focal points of future surveys rather than definitive conclusions.
Second, the results suggest directions for additional work aimed at enhancing the university
curriculum. For example, a review of the curriculum could be done in order to assess the extent to
which courses aid in developing soft skills, as well as whether syllabi provide for students to
engage in learning activities that lead to working software that they could use as evidence of their
qualifications. In addition, students who already have graduated could be invited into the
classroom to talk about what projects they did, during their time as students, that proved most
useful for convincing employers to hire them. Additional research could measure the extent to
which such guidance prompts students attempt to put this guidance into practice, the extent to
which they succeed, and whether these projects do in fact lead to impacts on employment
obtained immediately after graduation.
Finally, future work could follow up on the fact that, among all the skills and attributes rated by
respondents, the passion to learn was valued most highly. One interviewee in the prior study
explicitly contrasted this with the “I don’t care” attitude all too common among recent graduates,
and other interviewees noted the difficulty of finding workers with a drive to learn. This is
disappointing, particularly considering the role that universities play in preparing students for
career success. Additional studies could aim to understand what instructional experiences help to
nurture and amplify this drive – as well as what instructional experiences quench it. Research
would clearly be valued on how to stimulate not only students’ immediate motivation to learn
within a given course, but also how to cultivate a passion for lifelong learning.
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AUTHOR
Christopher Scaffidi earned a Ph.D. in software engineering from Carnegie
Mellon University and is currently an associate professor of computer science in
the School of EECS at Oregon State University. His research interests are where
human-computer interaction and software engineering intersect. Most of his
current projects aim to help software users to create code for themselves, and to
effectively share that code with one another.