Intellectual assets, especially its relational forms, have become increasingly important to explain a firm's innovation. To examine relational forms of intellectual assets (IA), this study theoretically and empirically advances a concept of alliance management capability (AMC) to explain the value creation and capture aspects of a firm's innovation process. The concepts of value-creating alliance experiences (VCAE) and value capturing alliance experiences (VCPAE) were introduced in which a firm's ability to learn from these alliance experiences increases the firm's ability to discover and govern partnerships that bring the firm's innovations to market. Hypotheses were developed and empirically examined in the biotechnology industry. A contribution of this study is that a firm's VCAE and VCPAE introduce a greater 'openness' to a firm's innovation process. This openness enables a firm to better adapt and respond to the opportunities of the market and thus impact a firm's competitive advantage to innovate.

The notion of creative slack purposefully refers to the notion of organisational slack proposed by Penrose (1959), who suggested that managers in organisations always have some stock of unused resources that inevitably accumulate when developing projects and are the primary factors determining the growth and innovation of the firm. In this contribution, we aim at adding a new dimension to the notion of organisational slack. Our view is that in many innovative organisations the slack of unused ideas is essentially a creative one, which is accumulated in diverse communities through multiple projects. This creative slack is a key intangible asset and a source of knowledge creation and innovation. To explain how organisations may benefit from exploiting the creative slack accumulated by communities, we rely on the analysis of two case studies, that of the Hydro-Québec Research Institute (IREQ), and of Ubisoft Montreal.

The use of blended learning is well suited for classes that involve a high level of experiential inquiry such as internship courses. These courses allow students to combine applied, face-to-face fieldwork activities with a reflective academic component delivered online. Therefore, the purpose of this article is to describe the pedagogical design and implementation of a pilot blended learning format internship course. After implementation, the pilot class was assessed. Results of the survey and focus group revealed high levels of student satisfaction in the areas of course structure, faculty-student interaction, and application of theory to the “real-world” experience undertaken by students during the internship. Lower levels of satisfaction with the course’s academic rigor and a sense of community were also reported. Notably, students with experience in blended learning expressed lower levels of overall satisfaction, but reported higher levels of satisfaction with the course’s rigor and sense of community. The paper concludes by offering implications for instructors seeking to implement blended learning approaches.

Teaching advanced programming can be a challenge, especially when the students are pursuing different majors with diverse analytical and problem-solving capabilities. The purpose of this paper is to explore the efficacy of using a particular problem as a vehicle for imparting a broad set of programming concepts and problem-solving techniques. We present a classic brain teaser that is used to communicate and demonstrate advanced software development concepts and techniques. Our results show that students with varied academic experiences and goals, assuming at least one procedural/structured programming pre-requisite, can benefit from and also be challenged by such an exercise. Although this problem has been used by others in the classroom, we believe that our use of this problem in imparting such a broad range of topics to a diverse student population is unique.

The paper describes the development of four learning modules that focus on technical details of how a person’s privacy might be compromised in real-world scenarios. The paper shows how students benefited from the addition of hands-on learning experiences of privacy and data protection to the existing information technology courses. These learning modules raised students’ awareness of potential breaches of privacy as a user as well as a developer. The demonstration of a privacy breach in action helped students to design, configure, and implement technical solutions to prevent privacy violations. The assessment results demonstrate the strength of the technical approach.

Aim/Purpose: This study aimed to examine whether there is a difference between manual feedback and online feedback with regard to feedback quality, respondents’ percentage, reliability and the amount of verbal comments written by students. Background: The quality of teaching is an important component of academic work. There are various methods for testing the quality of teaching; one of these methods is through students’ feedback. Methodology: This study used a quantitative approach, including the quantification of qualitative verbal data collected through an open question in the questionnaire. A sample of 180 courses was randomly chosen, 90 courses were evaluated manually and 90 were evaluated online. The number of students ranges from 7 to 60 students per course. In total 4678 students participated in the study. Contribution: The findings show that there is almost an identical pattern of feedback of manual and online course teaching evaluation. These findings encourage a continued use of this evaluation method. Findings: No significant differences were found between manual feedback and online feedback in the students’ evaluation of the lecturer/course. The percentage of respondents was significantly higher in the manual feedback than in the online feedback. The number of qualitative comments was significantly greater in the online feedback than in the manual feedback. Impact on Society: The findings of this study refute the claims with regard to the unreliability of an online teaching evaluation. These findings reflect the advantages of using online feedback, such as cost savings, granting more time to students in order to provide feedback, and reducing disturbance during lectures. Future Research: The gender aspect was not taken into account in the study. Therefore, we recommend conducting a follow-up study that will examine gender differences in directions of- difference between male and female lecturers, and differences between male and female students in teaching evaluation.

Aim/Purpose: To examine the early perceptions (acceptability) and usability of EASEL (Education through Application-Supported Experiential Learning), a mobile platform that delivers reflection prompts and content before, during, and after an experiential learning activity. Background: Experiential learning is an active learning approach in which students learn by doing and by reflecting on the experience. This approach to teaching is often used in disciplines such as humanities, business, and medicine. Reflection before, during, and after an experience allows the student to analyze what they learn and why it is important, which is vital in helping them to understand the relevance of the experience. A just-in-time tool (EASEL) was needed to facilitate this. Methodology: To inform the development of a mobile application that facilitates real-time guided reflection and to determine the relevant feature set, we conducted a needs analysis with both students and faculty members. Data collected during this stage of the evaluation helped guide the creation of a prototype. The user experience of the prototype and interface interactions were evaluated during the usability phase of the evaluation study. Contribution: Both the needs analysis and usability assessment provided justification for continued development of EASEL as well as insight that guides current development. Findings: The interaction design of EASEL is understandable and usable. Both students and teachers value an application that facilitates real-time guided reflection. Recommendations for Practitioners: The use of a system such as EASEL can leverage time and location-based services to support students in field experiences. This technology aligns with evidence that guided reflection provides opportunities for metacognition. Recommendation for Researchers: Iterative prototyping, testing, and refinement can lead to a deliberate and effective app development process. Impact on Society: The EASEL platform leverages inherent functionality of mobile devices, such as GPS and persistent network connectivity, to adapt reflection tasks based on lo-cation or time. Students using EASEL will engage in guided reflection, which leads to metacognition and can help instructors scaffold learning Future Research: We will continue to advance the application through iterative testing and development. When ready, the application will be vetted in larger studies across varied disciplines and contexts.

Aim/Purpose: To identify positive and negative aspects for learning of interactive tablet technology learning activities that promote student engagement and learning. Background: Engaging students in mathematics classes is an on-going challenge for teachers. In 2008 we were offered the opportunity to run interactive activities with a class set of tablet PCs that had just been released on to the market. Since then, we have run these interactive activities continuously with mathematics classes for computing students, albeit with two changes in hardware. Methodology: In the interactive activities, students submit full worked solutions to various problem types (classified as table, text, open or multi-choice) which can then be displayed to the class anonymously, discussed and annotated by the teacher. We surveyed student and staff perceptions and monitored academic performance. Contribution: We have over 10 years of results, observations, and experience from 2008, when tablet technologies were new and expensive, to the current time, when modern tablets with styli are now affordable. Findings: There was a significant increase in higher grades although pass rates did not increase significantly. Over the ten year period of the study, perceptions of students and staff about how this technology impacted on student learning were consistently positive. The majority of students found all problem types useful for learning even those they rated “too hard” or “too easy”. Benefits included increased feedback, peer learning and engagement. Recommendations for Practitioners: We recommend using tablet learning activities to engage students and teachers and to contribute to learning. Impact on Society: This study shows how using tablet technologies for interactive classroom activities can enable and enhance known pedagogies of feedback, peer instruction, and student engagement for mathematics classes. Future Research: We recommend extending this study to include larger classes, and other technical subjects that use symbols and diagrams. In addition, we suggest considering control groups.

Aim/Purpose: The study examined types of errors made by novice programmers in different Java concepts with students of different ability levels in programming as well as the perceived causes of such errors. Background: To improve code writing and debugging skills, efforts have been made to taxonomize programming errors and their causes. However, most of the studies employed omnibus approaches, i.e. without consideration of different programing concepts and ability levels of the trainee programmers. Such concepts and ability specific errors identification and classifications are needed to advance appropriate intervention strategy. Methodology: A sequential exploratory mixed method design was adopted. The sample was an intact class of 124 Computer Science and Engineering undergraduate students grouped into three achievement levels based on first semester performance in a Java programming course. The submitted codes in the course of second semester exercises were analyzed for possible errors, categorized and grouped across achievement level. The resulting data were analyzed using descriptive statistics as well as Pearson product correlation coefficient. Qualitative analyses through interviews and focused group discussion (FGD) were also employed to identify reasons for the committed errors. Contribution:The study provides a useful concept-based and achievement level specific error log for the teaching of Java programming for beginners. Findings: The results identified 598 errors with Missing symbols (33%) and Invalid symbols (12%) constituting the highest and least committed errors respec-tively. Method and Classes concept houses the highest number of errors (36%) followed by Other Object Concepts (34%), Decision Making (29%), and Looping (10%). Similar error types were found across ability levels. A significant relationship was found between missing symbols and each of Invalid symbols and Inappropriate Naming. Errors made in Methods and Classes were also found to significantly predict that of Other Object concepts. Recommendations for Practitioners: To promote better classroom practice in the teaching of Java programming, findings for the study suggests instructions to students should be based on achievement level. In addition to this, learning Java programming should be done with an unintelligent editor. Recommendations for Researchers: Research could examine logic or semantic errors among novice programmers as the errors analyzed in this study focus mainly on syntactic ones. Impact on Society: The digital age is code-driven, thus error analysis in programming instruction will enhance programming ability, which will ultimately transform novice programmers into experts, particularly in developing countries where most of the software in use is imported. Future Research: Researchers could look beyond novice or beginner programmers as codes written by intermediate or even advanced programmers are still not often completely error free.

Aim/Purpose: The purpose of this study is to develop an efficient methodology that can assist the evaluators in assessing a variable number of individuals that are working in groups and guarantee that the assessment is dependent on the group members’ performance and contribution to the work developed. Background: Collaborative work has been gaining more popularity in academic settings. However, group assessment needs to be performed according to each individual’s performance. The problem rests on the need to distinguish each member of the group in order to provide fair and unbiased assessments. Methodology: Design Science Research methodology supported the design of a framework able to provide the evaluator with the means to distinguish individuals in a workgroup and deliver fair results. Hevner’s DSR guidelines were fulfilled in order to describe WebAVALIA. To evaluate the framework, a quantitative study was performed and the first results are presented. Contribution: This paper provides a methodological solution regarding a fair evaluation of collaborative work through a tool that allows its users to perform their own assessment and peer assessment. These are made accordingly to the user’s perspectives on the performance of each group member throughout the work development. Findings: The first analysis of the results indicates that the developed method provides fairness in the assessment of group members, delivering a distinction amongst individuals. Therefore, each group member obtains a mark that corresponds to their specific contribution to the workgroup. Recommendations for Practitioners: For those who intend to apply this workgroup assessment method, it is relevant to raise student awareness about the methodology that is going to be used. That is, all the functionalities and steps in WebAVALIA have to be thoroughly explained before beginning of the project. Then, the evaluators have to decide about the students’ intermediate voting, namely if the evaluator chooses or not to publish student results throughout the project’s development. If there is the decision to display these intermediate results, the evaluator must try to encourage collaboration among workgroup members, instead of competition. Recommendation for Researchers: This study explores the design and development of an e-assessment tool – WebAVALIA. In order to assess its feasibility, its use in other institutions or contexts is recommended. The gathering of user opinions is suggested as well. It would then be interesting to compare the findings of this study with the results from other experimentations Impact on Society: Sometimes, people develop a rejection of collaborative work because they feel exploited due to the biased evaluation results. However, the group members assessment distinction, according to each one’s performance, may give each individual a sense of fairness and reward, leading to an openness/willingness towards collaborative work. Future Research: As future work, there are plans to implement the method in other group assessment contexts – such as sports and business environments, other higher education institutions, technical training students – in other cultures and countries. From this myriad of contexts, satisfaction results would be compared. Other future plans are to further explore the mathematical formulations and the respective WebAVALIA supporting algorithms.

Aim/Purpose: During the education of future engineers and experts in the field of computer science and information communication technology, the achievement of learning outcomes related to different levels of cognitive ability and knowledge dimensions can be a challenge. Background: Teachers need to design an appropriate set of activities for students and combine theory-based knowledge acquisition with practical training in technical skills. Including various activities for formative assessment during the course can positively affect students’ motivation for learning and ensure appropriate and timely feedback that will guide students in further learning. Methodology: The aim of the research presented in this paper is to propose an approach for course delivery in the field of software engineering and to determine whether the use of the approach increases student’s academic achievement. Using the proposed approach, the course Process Modeling for undergraduate students was redesigned and experimental study was conducted. Course results of the students (N=82) who took the new version of the course (experimental group) were compared to the results of the students from the control group (N=66). Contribution: An approach for a blended learning course in the field of software engineering was developed. This approach is based on the formative assessment activities that promote collaboration and the use of digital tools. Newly designed activities are used to encourage a greater level of acquired theoretical content and enhance the acquisition of subject-specific skills needed for practical tasks. Findings: The results showed that students who participated in the formative assessment activities achieved significantly better results. They had significantly higher scores in the main components of assessment compared to the students from the control group. In addition, students from the experimental group expressed positive views about the effectiveness of the used approach. Recommendations for Practitioners: The proposed approach has potential to increase students’ motivation and academic achievements so practitioners should consider to apply it in their own context. Recommendation for Researchers: Researchers are encouraged to conduct additional studies to explore the effectiveness of the approach with different courses and participants as well as to provide further insights regarding its applicability and acceptance by students. Impact on Society: The paper provides an approach and an example of good practice that may be beneficial for the university teachers in the field of computer science, information-communication technology, and engineering. Future Research: In the future, face-to-face activities will be adapted for performance in an online environment. Future work will also include a research on the possibilities of personalization of activities in accordance with the students’ characteristics.

Aim/Purpose: Business analytics is a cross-functional field that is important to implement for a college and has emerged as a critically important core component of the business curriculum. It is a difficult task due to scheduling concerns and limits to faculty and student resources. This paper describes the process of creating a central video repository to serve as a platform for just in time teaching and the impact on student learning outcomes. Background: Industry demand for employees with analytical knowledge, skills, and abilities requires additional analytical content throughout the college of business curriculum. This demand needs other content to be added to ensure that students have the prerequisite skills to complete assignments. Two pedagogical approaches to address this issue are Just-in-Time Teaching (JiTT) and scaffolding, grounded in the Vygoskian concept of “Zone of Proximal Development. Methodology: This paper presents a case study that applies scaffolding and JiTT teaching to create a video repository to add business analytics instruction to a curriculum. The California Critical Thinking Skills Test (CCTST) and Major Field Test (MFT) scores were analyzed to assess learning outcomes. Student and faculty comments were considered to inform the results of the review. Contribution: This paper demonstrates a practical application of scaffolding and JiTT theory by outlining the process of using a video library to provide valuable instructional resources that support meaningful learning, promote student academic achievement, and improve program flexibility. Findings: A centrally created library is a simple and inexpensive way to provide business analytics course content, augmenting standard content delivery. Assessment of learning scores showed an improvement, and a summary of lessons learned is provided to guide implications. Recommendations for Practitioners: Pedagogical implications of this research include the observation that producing a central library of instructor created videos and assignments can help address knowledge and skills gaps, augment the learning of business analytics content, and provide a valuable educational resource throughout the college of business curriculum. Recommendation for Researchers: This paper examines the use of scaffolding and JiTT theories. Additional examination of these theories may improve the understanding and limits of these concepts as higher education evolves due to the combination of market forces changing the execution of course delivery. Impact on Society: Universities are tasked with providing new and increasing skills to students while controlling the costs. A centrally created library of instructional videos provides a means of delivering meaningful content while controlling costs. Future Research: Future research may examine student success, including the immediate impact of videos and longitudinally using video repositories throughout the curriculum. Studies examining the approach across multiple institutions may help to evaluate the success of video repositories. Faculty acceptance of centrally created video libraries and assignments should be considered for the value of faculty recruiting and use in the classroom. The economic impact on both the university and students should be evaluated.

Aim/Purpose: This paper focuses on designing and implementing the rubric for objective JAVA programming assessments. An unsupervised learning approach was used to group learners based on their performance in the results obtained from the rubric, reflecting their learning ability. Background: Students' learning outcomes have been evaluated subjectively using a rubric for years. Subjective assessments are simple to construct yet inconsistent and biased to evaluate. Objective assessments are stable, reliable, and easy to conduct. However, they usually lack rubrics. Methodology: In this study, a Top-Down assessment approach is followed, i.e., a rubric focused on the learning outcome of the subject is designed, and the proficiency of learners is judged by their performance in conducting the task given. A JAVA rubric is proposed based on the learning outcomes like syntactical, logical, conceptual, and advanced JAVA skills. A JAVA objective quiz (with multiple correct options) is prepared based on the rubric criteria, comprising five questions per criterion. The examination was conducted for 209 students (100 from the MCA course and 109 from B.Tech. course). The suggested rubric was used to compute the results. K-means clustering was applied to the results to classify the students according to their learning preferences and abilities. Contribution: This work contributes to the field of rubric designing by creating an objective programming assessment and analyzing the learners’ performance using machine learning techniques. It also facilitates a reliable feedback approach offering various possibilities in student learning analytics. Findings: The designed rubric, partial scoring, and cluster analysis of the results help us to provide individual feedback and also, group the students based on their learning skills. Like on average, learners are good at remembering the syntax and concepts, mediocre in logical and critical thinking, and need more practice in code optimization and designing applications. Recommendations for Practitioners: The practical implications of this work include rubric designing for objective assessments and building an informative feedback process. Faculty can use this approach as an alternative assessment measure. They are the strong pillars of e-assessments and virtual learning platforms. Recommendation for Researchers: This research presents a novel approach to rubric-based objective assessments. Thus, it provides a fresh perspective to the researchers promising enough opportunities in the current era of digital education. Impact on Society: In order to accomplish the shared objective of reflective learning, the grading rubric and its accompanying analysis can be utilized by both instructors and students. As an instructional assessment tool, the rubric helps instructors to align their pedagogies with the students’ learning levels and assists students in updating their learning paths based on the informative topic-wise scores generated with the help of the rubric. Future Research: The designed rubric in this study can be extended to other programming languages and subjects. Further, an adaptable weighted rubric can be created to execute a flexible and reflective learning process. In addition, outcome-based learning can be achieved by measuring and analyzing student improvements after rubric evaluation.

Aim/Purpose: This study seeks to present a learning model of discrete mathematics elements, elucidate the content of teaching, and validate the effectiveness of this learning in a digital education context. Background: Teaching discrete mathematics in the realm of digital education poses challenges, particularly in crafting the optimal model, content, tools, and methods tailored for aspiring computer science teachers. The study draws from both a comprehensive review of relevant literature and the synthesis of the authors’ pedagogical experiences. Methodology: The research utilized a system-activity approach and aligned with the State Educational Standard. It further integrated the theory of continuous education as its psychological and pedagogical foundation. Contribution: A unique model for instructing discrete mathematics elements to future computer science educators has been proposed. This model is underpinned by informative, technological, and personal competencies, intertwined with the mathematical bedrock of computer science. Findings: The study revealed the importance of holistic teaching of discrete mathematics elements for computer science teacher aspirants in line with the Informatics educational programs. An elective course, “Elements of Discrete Mathematics in Computer Science”, comprising three modules, was outlined. Practical examples spotlighting elements of mathematical logic and graph theory of discrete mathematics in programming and computer science were showcased. Recommendations for Practitioners: Future computer science educators should deeply integrate discrete mathematics elements in their teaching methodologies, especially when aligning with professional disciplines of the Informatics educational program. Recommendation for Researchers: Further exploration is recommended on the seamless integration of discrete mathematics elements in diverse computer science curricula, optimizing for varied learning outcomes and student profiles. Impact on Society: Enhancing the quality of teaching discrete mathematics to future computer science teachers can lead to better-educated professionals, driving advancements in the tech industry and contributing to societal progress. Future Research: There is scope to explore the wider applications of the discrete mathematics elements model in varied computer science sub-disciplines, and its adaptability across different educational frameworks.

This study aims to examine the intellectual capital and its effect on the financial performance of Ethiopian private commercial banks using the pulic model. Quantitative panel data from audited annual reports of Ethiopian private commercial banks from 2011 to 2019 are collected. The robust fixed effect regression model has been adopted to investigate the effect of IC and the financial performance measures of the banks. The study results show a positive relationship between the value added intellectual coefficient (VAIC) and the financial performance of private commercial banks in Ethiopia. The study also revealed that the components of VAIC (i.e., human capital efficiency, capital employed efficiency, and structural capital efficiency) have a positive and significant effect on the financial performance of banks measured by return on asset and return on equity over the study periods. Practically, the results of the study could be useful for shareholders to consider IC as a strategic resource and hence emphasise these intangibles, and to the bank managers to benchmark themselves against the best competitors based on the level of efficiency rankings.

This paper explores innovative ideas and strategies for promoting environmental sustainability within the hotel industry, with the goal of streamlining these concepts for practical application in the industry and facilitating future academic research. The research methodology encompassed extensive online desk research, yielding a collection of 87 articles that were subject to thorough analysis. Additionally, personal consultations were conducted with industry experts to align their insights with the identified innovative ideas. To facilitate comprehension, appropriate terminology was assigned to these concepts. Subsequently, a post-discussion phase was conducted, engaging in one-on-one sessions with five industry experts to distil these insights into four distinct directions. This paper holds potential value for both industry stakeholders and academics, serving as a structured compendium of ideas and innovations crucial for advancing sustainability in the hotel sector. Moreover, it provides a solid foundation for further academic research.

This research aims to establish a valid and accurate measurement scale and identify consumer-driven characteristics for minimalism. The study has employed a hybrid approach to produce items for minimalism. Expert interviews were conducted to identify the items for minimalism in the first phase followed by consumer survey to obtain their response in second phase. A five-point Likert scale was used to collect the data. Further, data was subjected to reliability and validity check. Structural equation modelling was used to test the model. The findings demonstrated that there are five dimensions by which consumers perceive minimalism: decluttering, mindful consumption, aesthetic choices, financial freedom, and sustainable lifestyle. The outcome also revealed a high correlation between simplicity and well-being. This study is the first to provide a reliable and valid instrument for minimalism. The results will have several theoretical and practical ramifications for society and policymakers. It will support policymakers in gauging and encouraging minimalistic practices, which enhance environmental performance and lower carbon footprint.

This study explores the connection between mentorship and sustainable development (SD) within three major perspectives of sustainable development, such as social, environmental, and economic perspectives from China. Second, the study revealed the relationship between entrepreneurship and SD. Third, a moderation influence of innovation management (IM) was observed among the proposed nexuses of mentorship, entrepreneurship, and SD. To this end, a total of 535 questionnaires were eventually utilised with the support of SmartPLS and the structure equation modelling (SEM) approach. A positive connection was confirmed between mentorship and SD. The outcome uncovered a positive correlation between entrepreneurship and SD. In addition, a moderation of IM was found between mentorship, entrepreneurship, and SD. The study enlists several interesting lines about mentorship, entrepreneurship, and IM that might help to improve SD in terms of social, environmental, and economic perspectives. Besides, the study provides various implications for management and states the weaknesses along with the future directions for worldly researchers.

Evaluation of research artefacts (such as models, frameworks and methodologies) is essential to determine their quality and demonstrate worth. However, in the information quality (IQ) research domain there is no existing standard set of criteria available for researchers to use to evaluate their IQ artefacts. This paper therefore describes our experience of selecting and synthesising a set of evaluation criteria used in three related research areas of information systems (IS), software products (SP) and conceptual models (CM), and analysing their relevance to different types of IQ research artefact. We selected and used a subset of these criteria in an actual evaluation of an IQ artefact to test whether they provide any benefit over a standard evaluation. The results show that at least a subset of the criteria from the other domains of IS, SP and CM are relevant for IQ artefact evaluations, and the resulting set of criteria, most importantly, enabled a more rigorous and systematic selection of what to evaluate.

With the rapid development of Artificial Intelligence (AI) technology, significant breakthroughs have been made in its application in many fields. Especially, in the field of enterprise human resource management and employee performance evaluation, AI has demonstrated its powerful ability to optimise and improve performance. This study explores the application of AI in enterprise human resource management and how to use AI to evaluate employee performance. The research includes analysing and comparing existing AI-driven human resource management models, evaluating how AI can help improve employee performance and leadership styles, and designing and developing human resource management computer systems for enterprise employees. Through empirical research and case analysis, this study proposes a new AI-optimised employee performance evaluation model and explores its application and effect in practice. In general, the application of AI can improve the efficiency and accuracy of enterprise human resource management, and provide new possibilities for employee performance evaluation. At present, artificial intelligence technology has been widely used in various fields of daily life, especially in corporate human resource management, providing better support for the development of enterprises.

Voice Activity Detection (VAD) distinguishes speech segments from noise or silence areas. An efficient and noise-robust VAD system can be widely used for emerging speech technologies such as wireless communication and speech recognition. In this paper, we propose two versions of an unsupervised Arabic VAD method based on the combination of the Short-Time Energy (STE) and the Spectral Centroid (SC) features for formulating a typical threshold to detect speech areas. The first version compares only the STE feature to the threshold (STE-VAD). In contrast, the second compares the SC vector and the threshold (SC-VAD). The two versions of our VAD method were tested on 770 sentences of the Arabphone corpus, which were recorded in clean and noisy environments and evaluated under different values of Signal-to-Noise-Ratio. The experiments demonstrated the robustness of the STE-VAD in terms of accuracy and Mean Square Error.

This article presents a case study that outlines the potential of DigiBot technology, an interactive automated response program, in mobile open learning (MOL) for business subjects. The study, which draws on a project implemented in Sub-Saharan Africa, demonstrates the applications of DigiBots delivered via WhatsApp to over 650,000 learners. Employing a mixed-methods approach, the article reports on live event tracking, qualitative observations from facilitators and learning technologists, and a learner survey (<i>N</i> = 304,000). The research offers practical recommendations and proposes a model for scalable DigiBot learning. Findings reveal that in this case, DigiBot MOL had the potential to effectively address two key obstacles in open learning: accessibility and scalability. Leveraging mobile platforms such as WhatsApp mitigates accessibility restrictions, particularly in resource-constrained contexts, while tailored micro-learning enhances scalability.

Though there have been many approaches to dealing with social problems in recent years, the concepts of social value have yet to be discussed thoroughly. Upon examining these concepts in existing studies and testing them with two case studies, the article shows that there is the possibility that a group's shared wants may not be widely recognised as a social problem, and targeting these unserved populations is a precondition for solving social issues. It is essential to identify hidden social problems by understanding what is still left, the number of people sharing the same want, the severity of the unmet want, and the possible resources for solution generation. Social value in its narrower definition means meeting the satisfaction of the group sharing the same want, while in its broader definition, it means meeting the satisfaction of wider society. Finding workable solutions involves not only the group of people sharing the same want but also others who do not have the same want but who do recognise the importance of acknowledging the want of the subgroup.

Aiming at the problems of long evaluation time and poor evaluation accuracy of existing evaluation methods, an improved decision tree-based evaluation method for the effectiveness of college online course teaching reform is proposed. Firstly, the teaching mode of college online course is analysed, and an evaluation system is constructed to ensure the applicability of the evaluation method. Secondly, AHP entropy weight method is used to calculate the weights of evaluation indicators to ensure the accuracy and authority of evaluation results. Finally, the evaluation model based on decision tree algorithm is constructed and improved by fuzzy neural network to further optimise the evaluation results. The parameters of fuzzy neural network are adjusted and gradient descent method is used to optimise the evaluation results, so as to effectively evaluate the effect of college online course teaching reform. Through experiments, the evaluation time of the method is less than 5 ms, and the evaluation accuracy is more than 92.5%, which shows that the method is efficient and accurate, and provides an effective evaluation means for the teaching reform of online courses in colleges and universities.

Aiming to improve the accuracy, recall, and F1 value of data classification, this paper proposes an applied university innovation and entrepreneurship data classification method based on the nearest neighbour criterion. Firstly, the decision tree algorithm is used to mine innovation and entrepreneurship data from applied universities. Then, dynamic weight is introduced to improve the similarity calculation method based on edit distance, and the improved method is used to realise data de-duplication to avoid data over fitting. Finally, the nearest neighbour criterion method is used to classify applied university innovation and entrepreneurship data, and cosine similarity is used to calculate the similarity between the samples to be classified and each sample in the training data, achieving data classification. The experimental results demonstrate that the proposed method achieves a maximum accuracy of 96.5% and an average F1 score of 0.91. These findings indicate a high level of accuracy, recall, and F1 value for data classification using the proposed method.

In order to improve the evaluation effect of current university teaching reform, a new method for evaluating the quality of university course teaching reform is proposed based on data mining algorithms. Firstly, the optimal data clustering criterion was used to select evaluation indicators and a quality evaluation system for university curriculum teaching reform was established. Next, a reform quality evaluation model is constructed using BP neural network, and the training process is improved through genetic algorithm to obtain the model weight and threshold of the optimal solution. Finally, the calculated parameters are substituted into the model to achieve accurate evaluation of the quality of university curriculum teaching reform. Selecting evaluation accuracy and evaluation efficiency as evaluation indicators, the practicality of the proposed method was verified through experiments. The experimental results showed that the proposed method can mine teaching reform data and evaluate the quality of teaching reform. Its evaluation accuracy is higher than 96.3%, and the evaluation time is less than 10ms, which is much better than the comparison method, fully demonstrating the practicality of the method.

Research on the quality evaluation of teaching reforms plays an important role in promoting improvements in teaching quality. Therefore, an evaluation method of teaching reform quality in colleges and universities based on big data analysis is proposed. A multivariate logistic model is used to select the evaluation indicators for the quality evaluation of teaching reforms in universities. And clustering and cleaning of the evaluation indicator data are performed through big data analysis. The evaluation indicator data is used as input vectors, and the results of the teaching reform quality evaluation are used as output vectors. A support vector machine model based on the whale algorithm is built to obtain the relevant evaluation results. Experimental results show that the proposed method achieves a minimum recall rate of 98.7% for evaluation indicator data, the minimum data processing time of 96.3 ms, the accuracy rate consistently above 97.1%.

In order to improve the comprehensiveness of evaluation results and reduce errors, a teaching reform quality evaluation method based on fuzzy comprehensive evaluation is proposed. Firstly, on the premise of meeting the principles of indicator selection, factor analysis is used to construct an evaluation indicator system. Then, calculate the weights of various evaluation indicators through fuzzy entropy, establish a fuzzy evaluation matrix, and calculate the weight vector of evaluation indicators. Finally, the fuzzy cognitive mapping method is introduced to improve the fuzzy comprehensive evaluation method, obtaining the final weight of the evaluation indicators. The weight is multiplied by the fuzzy evaluation matrix to obtain the comprehensive evaluation result. The experimental results show that the maximum relative error of the proposed method's evaluation results is about 2.0, the average comprehensive evaluation result is 92.3, and the determination coefficient is closer to 1, verifying the application effect of this method.

In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds.

In order to overcome the problems of large error, low evaluation accuracy and long evaluation time in traditional evaluation methods of ideological and political education, this paper designs a quantitative evaluation method of ideological and political education achievements based on collaborative filtering algorithm. First, the evaluation index system is constructed to divide the teaching achievement evaluation index data in a small scale; then, the quantised dataset is determined and the quantised index weight is calculated; finally, the collaborative filtering algorithm is used to generate a set with high similarity, construct a target index recommendation list, construct a quantitative evaluation function and solve the function value to complete the quantitative evaluation of teaching achievements. The results show that the evaluation error of this method is only 1.75%, the accuracy can reach 98%, and the time consumption is only 2.0 s, which shows that this method can improve the quantitative evaluation effect.

The research goal is to solve the problems of low accuracy and long time existing in traditional teaching reform performance evaluation methods, a performance evaluation method of teaching reform based on hierarchical multi-task deep learning is proposed. Under the principle of constructing the evaluation index system, the evaluation indicator system should be constructed. The weight of the evaluation index is calculated through the analytic hierarchy process, and the calculation result of the evaluation weight is taken as the model input sample. A hierarchical multi-task deep learning model for teaching reform performance evaluation is built, and the final teaching reform performance score is obtained. Through relevant experiments, it is proved that compared with the experimental comparison method, this method has the advantages of high evaluation accuracy and short time, and can be further applied in relevant fields.

In order to overcome the problems of poor evaluation accuracy and long evaluation time in traditional customer relationship evaluation methods, this study proposes a new customer relationship evaluation method for e-commerce platform based on decision tree algorithm. Firstly, analyse the connotation and characteristics of customer relationship; secondly, the importance of customer relationship in e-commerce platform is determined by using decision tree algorithm by selecting and dividing attributes according to the information gain results. Finally, the decision tree algorithm is used to design the classifier, the weighted sampling method is used to obtain the training samples of the base classifier, and the multi-period excess income method is used to construct the customer relationship evaluation function to achieve customer relationship evaluation. The experimental results show that the accuracy of the customer relationship evaluation results of this method is 99.8%, and the evaluation time is only 51 minutes.

In view of the low evaluation accuracy of the effectiveness of cross-border e-commerce supply chain innovation model and the low correlation coefficient of innovation model influencing factors, the evaluation method of cross-border e-commerce supply chain innovation model based on blockchain technology is studied. First, analyse the operation mode of cross-border e-commerce supply chain, and determine the key factors affecting the innovation mode; Then, the comprehensive integration weighting method is used to analyse the factors affecting innovation and calculate the weight value; Finally, the blockchain technology is introduced to build an evaluation model for the supply chain innovation model and realise the evaluation of the cross-border e-commerce supply chain innovation model. The experimental results show that the evaluation accuracy of the proposed method is high, and the highest correlation coefficient of the influencing factors of innovation mode is about 0.99, which is feasible.

In order to solve the problems of large generalisation error, low recall rate and low retrieval accuracy of customer evaluation information in traditional trust evaluation methods, an evaluation method of customer trust in e-commerce market based on entropy weight analytic hierarchy process was designed. Firstly, build an evaluation index system of customer trust in e-commerce market. Secondly, the customer trust matrix is established, and the index weight is calculated by using the analytic hierarchy process and entropy weight method. Finally, five-scale Likert method is used to analyse the indicator factors and establish a comment set, and the trust evaluation value is obtained by combining the indicator membership. The experiment shows that the maximum generalisation error of this method is only 0.029, the recall rate is 97.5%, and the retrieval accuracy of customer evaluation information is closer to 1.

In order to overcome the problems of low efficiency, low user satisfaction and poor customer growth rate under the traditional marketing strategy, this paper studies the innovative strategy of mobile payment business marketing strategy under the internet environment. First of all, study the status quo of mobile payment business marketing in the internet environment, obtain mobile payment business data through questionnaire survey, and analyse the problems in mobile payment business marketing. Secondly, build a user profile of mobile payment business marketing, and classify user attributes, consumption characteristics and user activity through K-means clustering method; Finally, the marketing strategy is innovated from three aspects: product marketing, pricing marketing and channel marketing. The results show that the marketing benefit after the application of this strategy is 19.52 million yuan, the user satisfaction can reach 98.9%, and the customer growth rate can reach 21.3%, improving the marketing benefit of mobile payment business.

Using information criteria is a common method for making a decision about which model to use for forecasting. There are many different methods for evaluating forecasting models, such as MAE, RMSE, MAPE, and Theil-U, among others. After the creation of AIC, AICc, HQ, BIC, and BICc, the two criteria that have become the most popular and commonly utilised are Bayesian IC and Akaike's IC. In this investigation, we are innovative in our use of exponential weighting to get the log-likelihood of the information criteria for model selection, which means that we propose assigning greater weight to more recent data in order to reflect their increased precision. All research data is from the major stock markets' daily observations, which include the USA (GSPC, DJI), Europe (FTSE 100, AEX, and FCHI), and Asia (Nikkei).

This research was carried out with an objective of studying the existing mushroom value chain, identifying demand-supply gap, carrying out SWOT analysis to explore challenges, proposing action plan and presenting finally standard operating procedure for enhancing value chain effectiveness. Data was collected from 71 actors identified in the oyster mushroom value chain in Tumakuru Taluk, Karnataka State, India and analysed. Analysis showed that there were five different models of value chain, and the shortest value chain was the most profitable one. Based on the respondents' perceptions, mushroom cultivation offers many opportunities such as creating employment, improving economic condition and diet. Meanwhile they face challenges like, pest attack, hike in input materials' prices, lack of technical guidance during farming, finance support, inefficient marketing system. There is a need to address demand-supply gap, invest more in facilities and related research, integrate all the actors in value chain to enhance productivity.

In the realm of digital data, vendor-supplied cloud systems will still leave the user with responsibility for curation of digital data. Some of the very tasks users thought they were delegating to the cloud vendor may be a requirement for users after all. For example, cloud vendors most often require that users maintain archival copies. Beyond the better known vendor cloud model, we examine curation in two other models: inhouse clouds, and what we call "open" clouds—which are neither inhouse nor vendor. In open clouds, users come aboard as participants or partners—for example, by invitation. In open cloud systems users can develop their own software and data management, control access, and purchase their own hardware while running securely in the cloud environment. To do so will still require working within the rules of the cloud system, but in some open cloud systems those restrictions and limitations can be walked around easily with surprisingly little loss of freedom. It is in this context that REDDnet (Research and Education Data Depot network) is presented as the place where the Texas Tech University (TTU)) Libraries have been conducting research on long-term digital archival storage. The REDDnet network by year's end will be at 1.2 petabytes (PB) with an additional 1.4 PB for a related project (Compact Muon Soleniod Heavy Ion [CMS-HI]); additionally there are over 200 TB of tape storage. These numbers exclude any disk space which TTU will be purchasing during the year. National Science Foundation (NSF) funding covering REDDnet and CMS-HI was in excess of $850,000 with $850,000 earmarked toward REDDnet. In the terminology we used above, REDDnet is an open cloud system that invited TTU Libraries to participate. This means that we run software which fits the REDDnet structure. We are beginning to complete the final design of our system, and starting to move into the first stages of construction. And we have made a decision to move forward and purchase one-half petabyte of disk storage in the initial phase. The concerns, deliberations and testing are presented here along with our initial approach.