Employability and Communication Skills : Triangulating Views of Employers, Lecturers and Undergraduates

Employability skills are known as soft skills and transferrable skills. Employability refers to skills, understandings, and personal attributes that increase graduates’ chances of employment and success in their chosen occupations (Yorke, 2004). Some of the skills listed under employability skills are resourcefulness, adaptability, and flexibility which are not only needed for adapting to work situations (Curtis & McKenzie, 2002). In a VUCA (volatility, uncertainty, complexity, and ambiguity) environment, there is a limit to what universities can equip graduates with, and they need to be able to continue learning to adjust to new situations and demands. According to the Secretary’s Commission on Achieving Necessary Skills (SCANS) in the USA (1992), employability skills can be divided into four clusters of basic skills, thinking skills, personal qualities, and workplace competence. These skills would give them an edge during interviews and increase their chances of getting employed. Malaysia has been experiencing graduate unemployability. Approximately 60% of graduates remain unemployed for minimum of a year after graduation (“Graduate Employability”, 2020). There are many factors that contribute to graduate unemployability such as lack of experience, language proficiency, communication skills, problem-solving skills, and critical thinking skills (Hanapi & Nordin, 2014; Lim et al., 2016; Nooriah & Zakiah, 2017; Ooi & Ting, 2017). Employers often specify good communication skills and interpersonal skills as top requirements in job advertisements (Bakar et al., 2007; Ooi & Ting, 2017). However, graduates lack problem solving skills, communication skills (Hanapi & Nordin, 2014) and technical knowledge (Lim et al., 2016). In a knowledge-based economy, employees need to be independent and self-motivated (Menand, 2014) to acquire the necessary knowledge, information and high skill levels to cope with the fast pace of technological change. There is currently scarcity of findings on whether universities and students are preparing themselves appropriately to meet the expectations of employers. The study investigated importance of employability and communication skills based on the views of employers, lecturers and students. The research questions were: (1) how good are university students in their employability and communication skills? and (2) do employers and lecturers agree on the most important skills an effective employee should have? The descriptive study involved the use of a questionnaire on employability skills and language skills (listening and speaking, reading and writing). The items were formulated using a five-point rating scale of (1) not at all, (2) to some extent, (3) just enough, (4) to a reasonable extent, and (5) to a great extent. In addition, the questionnaire required lecturers and employers to select the top 10 skills out of the 25 skills listed. The data were collected from 123 students, 26 lecturers from a public university, and 26 employers in Sarawak, East Malaysia. The students were mostly female (74.80% female, 25.20% male) and had weak to moderate language proficiency, measured using the Malaysian University English Test (MUET). There were slightly more males among lecturers (12 female, 14 male) and employers (11 female, 15 male). The average years of work experience for lecturers was 8.7 (range: 1-25) and for employers, the average was 5.6 (range: 1-15). For the analysis, means and frequencies were calculated for comparison of the three perspectives on the importance of communication and employability skills. The results showed that there was a difference among employers, lecturers, and students in their ratings of how good university students are in their employability and communication skills. The students overrated themselves in all three set of skills. Based on the mean scores, the students rated themselves as having a moderate level of employability (M=3.74), reading and writing skills (M=3.75), and listening and speaking skills (M=3.61). The lecturers rated the university students as having a moderate level of skills as well, but the mean scores were slightly lower than the students’ (employability, M= 3.54; reading and writing skills, M=3.49; listening and speaking skills, M=3.29). To the employers, only the fresh graduates’ listening and speaking skills were moderate but on the weak side (M=3.15). The employers found the fresh graduates’ reading and writing skills (M=2.97) and listening and speaking skills (M=2.92) to be slightly weak. Interestingly, the students and lecturers rated the graduates’ employability skills to be moderate but the employers considered them to be weak. Another contrast was the students’ listening and speaking skills, which the students and lecturers considered to be the lowest level, compared to employability and reading and writing skills. However, the employers considered the fresh graduates’ listening and speaking skills to be better than the other two skills. This comparison shows that there is a mismatch in the ratings of university students’ employability and communication skills given by employers, lecturers, and students. The employers’ expectation was higher than the lecturers’. In other words, most employers expect students to be ready to handle the demands of the workforce upon graduation but sadly, most graduates fell short of their expectations. The employers may feel that they have to spoon feed the graduates on various matters upon graduation and they prefer employees who have a strong set of communication and employability skills. Next, the results on the ranking of the important skills an effective employee should have also showed a mismatch in the perspectives of employers and lecturers. To the employers, the top two skills were time management and problem-solving aptitude, both of which were employability skills. To the lecturers, the top two skills were leadership qualities and teamwork spirit, which were also employability skills. The employers prioritised skills for efficient handling of work situations to meet deadlines but the lecturers focussed on skills for the completion of group work. The mismatch shows that lecturers and universities may have overlooked the need to train students to be versatile to solve problems and complete projects on time. Indeed, students often submit work late and are not independent enough to resolve questions concerning their projects on their own, and constantly have to consult lecturers. To increase graduate employability, universities need to collaborate strategically with the industry to resolve the mismatch of expectations, as other Malaysian studies have also found a mismatch (Nadarajah, 2021; Nesaratnam et al., 2020). However, because of the fast-changing work environment, students need to develop lifelong learning skills so that they can develop their expertise, knowledge base, and a lifelong learning mindset to stay relevant. References Bakar, A. R., Mohamed, S., & Hanafi, I. (2007). Employability skills: Malaysian employers perspectives. The International Journal of Interdisciplinary Social Sciences, 2(1), 263-274. Curtis, D. D., & McKenzie, P. (2002). Employability skills for Australian industry: Literature review and framework development. http://www.voced.edu.au/content/ngv33428 Graduate employability: A priority of the Education Ministry. (2020, February 18). News Straits Times. https://www.nst.com.my/news/nation/2020/02/566731/graduate-employability-priority-education-ministry Hanapi, Z., & Nordin, M. S. (2014). Unemployment among Malaysia graduates: Graduates’ attributes, lecturers’ competency and quality of education. Procedia - Social and Behavioral Sciences, 112, 1056-1063. https://doi.org/10.1016/j.sbspro.2014.01.1269 Lim, Y. M, Teck, H. L., Ching, S. Y., & Chui, C. L. (2016). Employability skills, personal qualities, and early employment problems of entry-level auditors: Perspectives from employers, lecturers, auditors, and students. Journal of Education for Business, 91(4), 185-192. https://doi.org/10.1080/08832323.2016.1153998 Menand, H. (2014). Critical instruction, student achievement, and nurturing of global citizens: Global and comparative education in context. In S. A. Lawrence (Ed.), Critical practice in P-12 education (pp. 1-23). Hershey: Information Science Reference. Nadarajah, J. (2021). Measuring the gap in employability skills among Malaysian graduates. International Journal of Modern Trends in Social Sciences, 4(15), 81-87. https://doi.org/10.35631/IJMTSS.415007 Nesaratnam, S., Salleh, W. H. W., Foo, Y. V., Hisham, W. M. W. S. W. (2020). Enhancing English proficiency and communication skills among Malaysian graduates through training and coaching. International Journal of Learning and Development, 10(4), 1-12. https://doi.org/10.5296/ijld.v10i4.17875 Nooriah, Y., & Zakiah, J. (2017). Development of graduates employability: The role of university and challenges. Jurnal Personalia Pelajar, 20, 15-32. Ooi, K. B., & Ting, S. H. (2015). Employers’ emphasis on technical skills and soft skills in job advertisements. The English Teacher, 44(1), 1-12. Secretary’s Commission on Achieving Necessary Skills (SCANS) (1992). Learning a living: a blueprint for high performance. A SCANS report for America 2000. Washington: U.S. Department of Labour. Yorke, M. (2004). Employability in higher education: what it is – what it is not. York: The Higher Education Academy/ESECT

Automated Semantic Understanding of Human Emotions in Writing and Speech

Affective Human Computer Interaction (A-HCI) will be critical for the success of new technologies that will prevalent in the 21st century. If cell phones and the internet are any indication, there will be continued rapid development of automated assistive systems that help humans to live better, more productive lives. These will not be just passive systems such as cell phones, but active assistive systems like robot aides in use in hospitals, homes, entertainment room, office, and other work environments. Such systems will need to be able to properly deduce human emotional state before they determine how to best interact with people. This dissertation explores and extends the body of knowledge related to Affective HCI. New semantic methodologies are developed and studied for reliable and accurate detection of human emotional states and magnitudes in written and spoken speech; and for mapping emotional states and magnitudes to 3-D facial expression outputs. The automatic detection of affect in language is based on natural language processing and machine learning approaches. Two affect corpora were developed to perform this analysis. Emotion classification is performed at the sentence level using a step-wise approach which incorporates sentiment flow and sentiment composition features. For emotion magnitude estimation, a regression model was developed to predict evolving emotional magnitude of actors. Emotional magnitudes at any point during a story or conversation are determined by 1) previous emotional state magnitude; 2) new text and speech inputs that might act upon that state; and 3) information about the context the actors are in. Acoustic features are also used to capture additional information from the speech signal. Evaluation of the automatic understanding of affect is performed by testing the model on a testing subset of the newly extended corpus. To visualize actor emotions as perceived by the system, a methodology was also developed to map predicted emotion class magnitudes to 3-D facial parameters using vertex-level mesh morphing. The developed sentence level emotion state detection approach achieved classification accuracies as high as 71% for the neutral vs. emotion classification task in a test corpus of children’s stories. After class re-sampling, the results of the step-wise classification methodology on a test sub-set of a medical drama corpus achieved accuracies in the 56% to 84% range for each emotion class and polarity. For emotion magnitude prediction, the developed recurrent (prior-state feedback) regression model using both text-based and acoustic based features achieved correlation coefficients in the range of 0.69 to 0.80. This prediction function was modeled using a non-linear approach based on Support Vector Regression (SVR) and performed better than other approaches based on Linear Regression or Artificial Neural Networks

A Corpus Driven Computational Intelligence Framework for Deception Detection in Financial Text

Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as £193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud firms of similar size and industrial grouping. Differently from other similar studies, this thesis uniquely takes a wide angled view and extracts a range of features of different categories from the corpus. These linguistic correlates of deception are uncovered using a variety of techniques and tools. Corpus linguistics methodology is applied to extract keywords and to examine linguistic structure. N-grams are extracted to draw out collocations. Readability measurement in financial text is advanced through the extraction of new indices that probe the text at a deeper level. Cognitive and perceptual processes are also picked out. Tone, intention and liquidity are gauged using customised word lists. Linguistic ratios are derived from grammatical constructs and word categories. An attempt is also made to determine ‘what’ was said as opposed to ‘how’. Further a new module is developed to condense synonyms into concepts. Lastly frequency counts from keywords unearthed from a previous content analysis study on financial narrative are also used. These features are then used to drive machine learning based classification and clustering algorithms to determine if they aid in discriminating a fraud from a non-fraud firm. The results derived from the battery of models built typically exceed classification accuracy of 70%. The above process is amalgamated into a framework. The process outlined, driven by empirical data demonstrates in a practical way how linguistic analysis could aid in fraud detection and also constitutes a unique contribution made to deception detection studies

Universal and language-specific processing : the case of prosody

A key question in the science of language is how speech processing can be influenced by both language-universal and language-specific mechanisms (Cutler, Klein, & Levinson, 2005). My graduate research aimed to address this question by adopting a crosslanguage approach to compare languages with different phonological systems. Of all components of linguistic structure, prosody is often considered to be one of the most language-specific dimensions of speech. This can have significant implications for our understanding of language use, because much of speech processing is specifically tailored to the structure and requirements of the native language. However, it is still unclear whether prosody may also play a universal role across languages, and very little comparative attempts have been made to explore this possibility. In this thesis, I examined both the production and perception of prosodic cues to prominence and phrasing in native speakers of English and Mandarin Chinese. In focus production, our research revealed that English and Mandarin speakers were alike in how they used prosody to encode prominence, but there were also systematic language-specific differences in the exact degree to which they enhanced the different prosodic cues (Chapter 2). This, however, was not the case in focus perception, where English and Mandarin listeners were alike in the degree to which they used prosody to predict upcoming prominence, even though the precise cues in the preceding prosody could differ (Chapter 3). Further experiments examining prosodic focus prediction in the speech of different talkers have demonstrated functional cue equivalence in prosodic focus detection (Chapter 4). Likewise, our experiments have also revealed both crosslanguage similarities and differences in the production and perception of juncture cues (Chapter 5). Overall, prosodic processing is the result of a complex but subtle interplay of universal and language-specific structure

Workshop Proceedings of the 12th edition of the KONVENS conference

The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years

Discourse models for collaboratively edited corpora

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 77-81).This thesis focuses on computational discourse models for collaboratively edited corpora. Due to the exponential growth rate and significant stylistic and content variations of collaboratively edited corpora, models based on professionally edited texts are incapable of processing the new data effectively. For these methods to succeed, one challenge is to preserve the local coherence as well as global consistence. We explore two corpus-based methods for processing collaboratively edited corpora, which effectively model and optimize the consistence of user generated text. The first method addresses the task of inserting new information into existing texts. In particular, we wish to determine the best location in a text for a given piece of new information. We present an online ranking model which exploits this hierarchical structure - representationally in its features and algorithmically in its learning procedure. When tested on a corpus of Wikipedia articles, our hierarchically informed model predicts the correct insertion paragraph more accurately than baseline methods. The second method concerns inducing a common structure across multiple articles in similar domains to aid cross document collaborative editing. A graphical model is designed to induce section topics and to learn topic clusters. Some preliminary experiments showed that the proposed method is comparable to baseline methods.by Erdong Chen.S.M

Negative vaccine voices in Swedish social media

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy creates concerns for a portion of the population in many countries, including Sweden. Since discussions on vaccine hesitancy are often taken on social networking sites, data from Swedish social media are used to study and quantify the sentiment among the discussants on the vaccination-or-not topic during phases of the COVID-19 pandemic. Out of all the posts analyzed a majority showed a stronger negative sentiment, prevailing throughout the whole of the examined period, with some spikes or jumps due to the occurrence of certain vaccine-related events distinguishable in the results. Sentiment analysis can be a valuable tool to track public opinions regarding the use, efficacy, safety, and importance of vaccination