Ashley Jensen and JK Simmons join Alex Jones and Vernon Kay on the sofa.

UK Border Force find 25kg of cocaine in a Darlington man's suitcase after he flies in from Mexico.

Airport bosses are predicting further growth in 2025

Councillor Ruth Houghton says the kerosene in the drains is "still flowing" after 10 days.

Farmers say the National Trust has damaged heathland on the Long Mynd.

The council says it needs to make savings immediately due to a predicted £28m overspend this year.

Bruno Fernandes went to the aid of a man who almost collapsed on a flight from Manchester to Lisbon.

Three mainstream schools in Herefordshire will benefit from the grant and a SEND school will be built.

The council says the route will close for five days, including to emergency vehicles.

The iPhone could finally show you how long it’ll take to finish charging. Code spotted in the second iOS 18.2 beta by 9to5Mac shows a new “BatteryIntelligence” feature that will let you […]

The post Apple to roll out ‘Battery Intelligence’ for iPhone, Amazon slashes price of 43inch Hisense smart TV to £228 appeared first on Tech Digest.

The back-row contenders come front and centre, Harlequins have a new Puma on the loose and more Premiership talking points

Books, booze and unique stocking fillers.

Listen to this week's podcast (October 29, 2011) (23:05 minutes) I'm trying a little something different this week. I hope you guys like pictures. :) Brenden Eich + "I Like Ike" mashup by @lonnen 140 byte synthesizer A while back Jed Schmidt created a simple little project on GitHub called 140 bytes ...

Esta entrada trata lo más representativo en el mundo de la T&I. Abarca las categorías de traductores, intérpretes, organismos y eventos.

La entrada LO MÁS REPRESENTATIVO DENTRO DEL MUNDO DE LA T&I se publicó primero en Nartran Translations.

Education choice is succeeding in other states

This work presents a first approach to the synthesis of Spanish Sign Language's (LSE) Classifier Constructions (CCs). All current attempts at the automatic synthesis of LSE simply create the animations corresponding to sequences of signs. This work, however, includes the synthesis of the LSE classification phenomena, defining more complex elements than simple signs, such as Classifier Predicates, Inflective CCs and Affixal classifiers. The intelligibility of our synthetic messages was evaluated by LSE natives, who reported a recognition rate of 93% correct answers.

E-mail spam is no more garbage but risk since it recently includes virus attachments and spyware agents which make the recipients' system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning techniques have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should counteract with it. To cope with this, parameters optimization and feature selection have been used to reduce processing overheads while guaranteeing high detection rates. However, previous approaches have not taken into account feature variable importance and optimal number of features. Moreover, to the best of our knowledge, there is no approach which uses both parameters optimization and feature selection together for spam detection. In this paper, we propose a spam detection model enabling both parameters optimization and optimal feature selection; we optimize two parameters of detection models using Random Forests (RF) so as to maximize the detection rates. We provide the variable importance of each feature so that it is easy to eliminate the irrelevant features. Furthermore, we decide an optimal number of selected features using two methods; (i) only one parameters optimization during overall feature selection and (ii) parameters optimization in every feature elimination phase. Finally, we evaluate our spam detection model with cost-sensitive measures to avoid misclassification of legitimate messages, since the cost of classifying a legitimate message as a spam far outweighs the cost of classifying a spam as a legitimate message. We perform experiments on Spambase dataset and show the feasibility of our approaches.

Malgré un télescopage plus que dommageable avec l’affaire Ferrand – il aurait déjà dû démissionner – François Bayrou a annoncé un train de mesures visant à moraliser visant à encadrer les élus. Et bien celles-ci vont dans le bon sens. Bien sûr, on pourra...

No Comment : les mariachis se réunissent à Mexico pour battre le record du monde de chant

1. Match France-Israël : Valérie Pécresse sera présente au Stade de France  Le Figaro
2. France-Israël : une gestion sécuritaire compliquée à Saint-Denis (Sécurité)  L'Équipe
3. Pourquoi Israël et ses clubs jouent-ils en Europe ?  Le Parisien
4. France - Israël : Pourquoi le Stade de France n'aura jamais été aussi vide  Eurosport FR
5. Comment suivre le match de Ligue des Nations France - Israël  20 Minutes

1. Senators make short work of listless Leafs in a flat Battle of Ontario  The Globe and Mail
2. Tavares on Maple Leafs’ lifeless loss to Senators: ‘We should be disappointed’  Sportsnet.ca
3. Ullmark records first shutout of season as Senators smother Maple Leafs  TSN
4. TAKEAWAYS: Ottawa Senators draw first blood in Battle of Ontario  Ottawa Citizen
5. Wednesday FTB: Lose to the Sens, why not?  Pension Plan Puppets

1. Democrat Ruben Gallego wins Arizona U.S. Senate race against Republican Kari Lake  The Globe and Mail
2. Ruben Gallego defeats Trump ally Kari Lake in Arizona Senate race  BBC.com
3. Democrat Gallego wins Arizona, Republicans hold 53-47 US Senate majority  Al Jazeera English

C'est malin. A peine nommￜ ministre de la Santￜ, Franￜois Braun croyait avoir trouvￜ une astuce pour dￜsengorger les services d'urgences des hￜpitaux : demander aux patients d'appeler le 15,...

La crise ? Quelle crise ? Alors que le gouvernement prￜpare l'opinion ￜ une longue pￜriode d'inflation et de hausse des prix de l'ￜnergie en raison de la guerre en Ukraine, tout ne va pas si mal sur le plan...

The intelligent financial sharing platform in the online realm is capable of collecting, storing, processing, analysing and sharing financial data through the integration of AI and big data processing technologies. However, as data volume grows exponentially, the cost of financial data storage and processing increases, and the asset accounting and financial profit data sharing analysis structure in financial sharing platforms is inadequate. To address the issue of data security sharing in the intelligent financial digital sharing platform, this paper proposes a data-sharing framework based on blockchain and edge computing. Building upon this framework, a non-separable task distribution algorithm based on data sharing is developed, which employs multiple nodes for cooperative data storage, reducing the pressure on the central server for data storage and solving the problem of non-separable task distribution. Multiple sets of comparative experiments confirm the proposed scheme has good feasibility in improving algorithm performance and reducing energy consumption and latency.

Aim/Purpose: The goal of the study was to examine the perceptions of senior academic staff who also serve as policymakers in Israeli colleges of education, regarding the integration of technology in teacher education, and the shift to online learning during the Covid-19 pandemic. There is little research on this issue and consequently, the aim of the present study is to fill this lacuna. Background: In Israel, senior academic staff in colleges of education play a particularly important role in formulating institutional policies and vision regarding the training of preservice teachers. They fulfil administrative functions, teach, and engage in research as part of their academic position. During the Covid-19, they led the shift to online learning. However, there is little research on their perceptions of technology integration in teacher education in general, and during the Covid-19, in particular. Methodology: This qualitative study conducted semi-structured interviews with 25 senior academic staff from 13 academic colleges of education in Israel. Contribution: The study has practical implications for the implementation of technology in teacher education, suggesting the importance of establishing open discourse and collaboration between college stakeholders to enable enactment of a vision for equity-that allows programs to move swiftly from crisis-management to innovation and transformation during the Covid-19 pandemic. Findings: The findings obtained from content analysis of the interviews reveals a central concept: “On both sides of the divide”, and points of intersection in the perceptions of the senior academic staff. The central concept encompassed three themes: (1) centralization - between top-down and bottom-up policies, (2) between innovation and conservation, and (3) between crisis and growth. The findings indicate that in times of crisis, the polarity surrounding issues essential to the organisation’s operation is reduced, and a blend is formed to create a new reality in which the various dichotomies merge. Recommendations for Practitioners: The study has practical implications for the scope of discussions on the implementation of technology in teacher education (formulating a vision and policies, and their translation into practice), suggesting that such discussions should consider the perceptions of policymakers. Recommendation for Researchers: The findings reflect the challenges faced by senior academic staff at colleges of education that reflect the ongoing attempts to negotiate and reconcile different concerns. Impact on Society: The findings have implications for colleges of education that are responsible for pre-service teachers' teaching practices. Future Research: An enacted vision for equity-based educator preparation that allows programs to move swiftly from crisis-management to innovation and transformation. Future research might reveal a more complete picture by investigating a broader spectrum of stakeholders both in Israel and elsewhere. Hence, future research should examine the power relations between senior college staff and external bodies such as the Higher Education Council (which determines higher education policies in Israel).

Aim/Purpose: The study focused on learner sentiments and experiences after using the Moodle assessment module and trained a machine learning classifier for future sentiment predictions. Background: Learner assessment is one of the standard methods instructors use to measure students’ performance and ascertain successful teaching objectives. In pedagogical design, assessment planning is vital in lesson content planning to the extent that curriculum designers and instructors primarily think like assessors. Assessment aids students in redefining their understanding of a subject and serves as the basis for more profound research in that particular subject. Positive results from an evaluation also motivate learners and provide employment directions to the students. Assessment results guide not just the students but also the instructor. Methodology: A modified methodology was used for carrying out the study. The revised methodology is divided into two major parts: the text-processing phase and the classification model phase. The text-processing phase consists of stages including cleaning, tokenization, and stop words removal, while the classification model phase consists of dataset training using a sentiment analyser, a polarity classification model and a prediction validation model. The text-processing phase of the referenced methodology did not utilise tokenization and stop words. In addition, the classification model did not include a sentiment analyser. Contribution: The reviewed literature reveals two major omissions: sentiment responses on using the Moodle for online assessment, particularly in developing countries with unstable internet connectivity, have not been investigated, and variations of the k-fold cross-validation technique in detecting overfitting and developing a reliable classifier have been largely neglected. In this study we built a Sentiment Analyser for Learner Emotion Management using the Moodle for assessment with data collected from a Ghanaian tertiary institution and developed a classification model for future sentiment predictions by evaluating the 10-fold and the 5-fold techniques on prediction accuracy. Findings: After training and testing, the RF algorithm emerged as the best classifier using the 5-fold cross-validation technique with an accuracy of 64.9%. Recommendations for Practitioners: Instead of a closed-ended questionnaire for learner feedback assessment, the open-ended mechanism should be utilised since learners can freely express their emotions devoid of restrictions. Recommendation for Researchers: Feature selection for sentiment analysis does not always improve the overall accuracy for the classification model. The traditional machine learning algorithms should always be compared to either the ensemble or the deep learning algorithms Impact on Society: Understanding learners’ emotions without restriction is important in the educational process. The pedagogical implementation of lessons and assessment should focus on machine learning integration Future Research: To compare ensemble and deep learning algorithms

Aim/Purpose: Despite playing a critical role in shaping the future, 70% of undergraduate engineers report low levels of motivation. Student disengagement and a lack of ownership of their learning are significant challenges in higher education, specifically engineering students in the computer science department. This study investigates the various causes of these problems among first-year undergraduate engineers. Background: Student disengagement has become a significant problem, especially in higher education, leading to reduced academic performance, lower graduation rates, and less satisfaction with learning. The study intends to develop approaches that encourage a more interesting and learner-motivated educational environment. Methodology: This research uses a mixed methods approach by combining quantitative data from a survey-based questionnaire with qualitative insights from focus groups to explore intrinsic and extrinsic motivators, instructional practices, and student perceptions of relevance and application of course content. The aim of this method is to make an all-inclusive exploration into undergraduate engineering students’ perspectives on factors contributing to this disengagement and the need for more ownership. Contribution: Inculcating passion for engineering among learners seems demanding, with numerous educational programs struggling with issues such as a lack of interest by students and no personal investment in learning. Understanding the causes is of paramount importance. The study gives suggestions to help teachers or institutions create a more engaged and ownership-based learning environment for engineering students. Findings: The findings revealed a tangled web influencing monotonous teaching styles, limited opportunities and applications, and a perceived gap between theoretical knowledge and real-world engineering problems. It emphasized the need to implement more active learning strategies that could increase autonomy and a stronger sense of purpose in their learning journey. It also highlights the potential use of technology in promoting student engagement and ownership. Further research is needed to explore optimal implementation strategies for online simulations, interactive learning platforms, and gamification elements in the engineering curriculum. Recommendations for Practitioners: It highlights the complex interplay of intrinsic and extrinsic motivation factors and the need to re-look at instructional practice and emphasize faculty training to develop a more student-centered approach. It also stresses the need to look into the relevance and application of the course content. Recommendation for Researchers: More work needs to be done with a larger, more diverse sample population across multiple institutions and varied sociocultural and economic backgrounds. Impact on Society: Enhancing learners’ educational experience can result in creating a passionate and competent team of engineers who can face future obstacles fearlessly and reduce the production of half-baked graduates unprepared for the profession’s challenges. Future Research: Conduct long-term studies to assess the impact of active learning and technology use on student outcomes and career readiness. Investigate scaling up successful strategies across diverse engineering programs. See if promising practices work well everywhere.

Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.

Aim/Purpose: This article examines how learning activities draw on resources in the work context to learn. Background The background is that if knowledge no longer is seen mainly as objects, but processes, how then to understand boundary objects? Our field study of learning activities reveals the use of pictures, documents and emotions for learning in the geographically distributed Norwegian Labor Inspection Authority Methodology: The study is a qualitative study consisting of interview data, observation data, and documents. Contribution: Contribute to practice based theorizing. Findings: Three ideal types of representing practices have been identified, i.e., ‘Visualizing’, ‘Documenting’ and ‘Testing’. All three are combined with storytelling, sensing, reflections and sensemaking, which point at the importance of processes in learning. The article also add insights about how emotions can be an important resource for boundary spanning – and sensemaking – by creating the capability of reflecting upon and integrating different knowledge areas in the in- practice context. Recommendations for Practitioners: Look for boundary objects within your field to promote online learning. Recommendation for Researchers: Study boundary objects in work context to understand learning. Impact on Society Role of objects in human learning. Future Research: Focus on how emotions can be used for online learning.

Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

Aim/Purpose: The objective of this research is to investigate the effect of review consistency between textual content and rating on review helpfulness. A measure of review consistency is introduced to determine the degree to which the review sentiment of textual content conforms with the review rating score. A theoretical model grounded in signaling theory is adopted to explore how different variables (review sentiment, review rating, review length, and review rating variance) affect review consistency and the relationship between review consistency and review helpfulness. Background: Online reviews vary in their characteristics and hence their different quality features and degrees of helpfulness. High-quality online reviews offer consumers the ability to make informed purchase decisions and improve trust in e-commerce websites. The helpfulness of online reviews continues to be a focal research issue regardless of the independent or joint effects of different factors. This research posits that the consistency between review content and review rating is an important quality indicator affecting the helpfulness of online reviews. The review consistency of online reviews is another important requirement for maintaining the significance and perceived value of online reviews. Incidentally, this parameter is inadequately discussed in the literature. A possible reason is that review consistency is not a review feature that can be readily monitored on e-commerce websites. Methodology: More than 100,000 product reviews were collected from Amazon.com and preprocessed using natural language processing tools. Then, the quality reviews were identified, and relevant features were extracted for model training. Machine learning and sentiment analysis techniques were implemented, and each review was assigned a consistency score between 0 (not consistent) and 1 (fully consistent). Finally, signaling theory was employed, and the derived data were analyzed to determine the effect of review consistency on review helpfulness, the effect of several factors on review consistency, and their relationship with review helpfulness. Contribution: This research contributes to the literature by introducing a mathematical measure to determine the consistency between the textual content of online reviews and their associated ratings. Furthermore, a theoretical model grounded in signaling theory was developed to investigate the effect on review helpfulness. This work can considerably extend the body of knowledge on the helpfulness of online reviews, with notable implications for research and practice. Findings: Empirical results have shown that review consistency significantly affects the perceived helpfulness of online reviews. The study similarly finds that review rating is an important factor affecting review consistency; it also confirms a moderating effect of review sentiment, review rating, review length, and review rating variance on the relationship between review consistency and review helpfulness. Overall, the findings reveal the following: (1) online reviews with textual content that correctly explains the associated rating tend to be more helpful; (2) reviews with extreme ratings are more likely to be consistent with their textual content; and (3) comparatively, review consistency more strongly affects the helpfulness of reviews with short textual content, positive polarity textual content, and lower rating scores and variance. Recommendations for Practitioners: E-commerce systems should incorporate a review consistency measure to rank consumer reviews and provide customers with quick and accurate access to the most helpful reviews. Impact on Society: Incorporating a score of review consistency for online reviews can help consumers access the best reviews and make better purchase decisions, and e-commerce systems improve their business, ultimately leading to more effective e-commerce. Future Research: Additional research should be conducted to test the impact of review consistency on helpfulness in different datasets, product types, and different moderating variables.

Aim/Purpose: This study aimed to increase our understanding of how the stages of the customer purchase journey, privacy trade-offs, and information sensitivity of different business service sectors affect consumers’ privacy concerns. Background: The study investigated young consumers’ willingness to provide consent to use their personal data at different phases of the customer journey. This study also examined their readiness to provide consent if they receive personal benefits, and how information sensitivity varied between different individuals and business sectors. Methodology: Data was collected by a quantitative survey (n=309) and analyzed with R using the Bayesian linear mixed effect modeling approach. The sample consisted of university students in Finland, who represented a group of young and digitally native consumers. The questionnaire was designed for this study and included constructs with primarily Likert-scale items. Contribution: The study contributed to data privacy and consent management research in information sensitivity, privacy trade-off, and the customer journey. The study underlined the need for a stronger user experience focus and contextuality. Findings: The results showed that readiness to disclose personal data varied at different phases of the customer journey as privacy concerns did not decrease in a linear fashion throughout the purchase process. Perceived benefits affected the willingness to provide consent for data usage, but concerned consumers would be less trade-off oriented. Self-benefit was the most relevant reason for sharing, while customization was the least. There is a connection between the information sensitivity of different business sector information and privacy concerns. No support for gender differences was found, but age affected benefits and business sector variables. Recommendations for Practitioners: The study recommends approaching consumers’ data privacy concerns from a customer journey perspective while trying to motivate consumers to share their personal data with relevant perceived benefits. The self-benefit was the most relevant benefit for willingness to provide consent, while customization was the least. Recommendation for Researchers: The study shows that individual preference for privacy was a major factor directly and via interaction for all three models. This study also showed that consumers’ subjective decision-making in privacy issues is both a situational and a contextual factor. Impact on Society: This study could encourage policymakers and societies to develop guidelines on how to develop privacy practices and consent management to be more user centric as individuals are increasingly concerned about their online privacy. Future Research: This study encourages examining consumers’ motivational factors to provide digital consent for companies with experimental research settings. This study also calls to explore perceived benefits in all age groups from the perspective of different information in various business sectors. This study shows that privacy concern is a contextual and situational factor.

Creative computing is one of the rapidly growing educational trends around the world. Previous studies have shown that creative computing can empower disadvantaged children and youth. At-risk youth tend to hold a negative view of self and perceive their abilities as inferior compared to “normative” pupils. The Implicit Theories of Intelligence approach (ITI; Dweck, 1999, 2008) suggests a way of changing beliefs regarding one’s abilities. This paper reports findings from an experiment that explores the impact of a short intervention among at-risk youth and “normative” high-school students on (1) changing ITI from being perceived as fixed (entity view of intelligence) to more flexible (incremental view of intelligence) and (2) the quality of digital self-representations programmed though a creative computing app. The participants were 117 Israeli youth aged 14-17, half of whom were at-risk youth. The participants were randomly assigned to the experimental and control conditions. The experimental group watched a video of a lecture regarding brain plasticity that emphasized flexibility and the potential of human intelligence to be cultivated. The control group watched a neutral lecture about brain-functioning and creativity. Following the intervention, all of the participants watched screencasts of basic training for the Scratch programming app, designed artifacts that digitally represented themselves five years later and reported their ITI. The results showed more incremental ITI in the experimental group compared to the control group and among normative students compared to at-risk youth. In contrast to the research hypothesis, the Scratch projects of the at-risk youth, especially in the experimental condition, were rated by neutral judges as being more creative, more aesthetically designed, and more clearly conveying their message. The results suggest that creative computing combined with the ITI intervention is a way of developing creativity, especially among at-risk youth. Increasing the number of youths who hold incremental views of intelligence and developing computational thinking may contribute to their empowerment and well-being, improve learning and promote creativity.

Aim/Purpose: The research work investigated the information seeking process of undergraduates in a specialised university in Nigeria, in the course of a group assignment. Background: Kuhlthau’s Information Search Process (ISP) model is used as lens to reveal how students interact with information in the affective, cognitive and physical realms. Methodology: Qualitative research methods were employed. The entire seventy-seven third year students in the Department of Petroleum and Natural Gas and their course lecturer were the participants. Group assignment question was analysed using Bloom’s Taxonomy while the information seeking process of the students was garnered through dialogue journaling on WhatsApp Messenger. Contribution: The research explicates how students’ information seeking behaviour can be captured beyond the four walls of a classroom by using a Web 2.0 tool such as WhatsApp Messenger. Findings: The apparent level of uncertainty, optimism, and confusion/doubt common in the initiation, selection, and exploration phases of the ISP model and low confidence levels were not markedly evident in the students. Consequently, Kuhlthau’s ISP model could not be applied in its entirety to the study’s particular context of teaching and learning due to the nature of the assignment. Recommendations for Practitioners: The study recommends that the Academic Planning Unit (APU) should set a benchmark for all faculties and, by extension, the departments in terms of the type/scope and number of assignments per semester, including learning outcomes. Recommendation for Researchers: Where elements of a guided approach to learning are missing, Kuhlthau’s ISP may not be employed. Therefore, alternative theory, such as Theory of Change could explain the poor quality of education and the type of intervention that could enhance students’ learning. Impact on Society: The ability to use emerging technologies is a form of literacy that is required by the 21st century work place. Hence, the study demonstrates students’ adaptation to emerging technology. Future Research: The study is limited to only one case site. It would be more helpful to the Nigerian society to have this study extended to other universities for the purpose of generalisation and appropriate intervention.

Aim/Purpose: This study aims to explore levels of Technological Access (ownership, access to, and usage of computer devices as well as access to Internet services) and levels of Technological Efficacy (technology related skills) as they pertain to underserved (UNS) and underrepresented (UNR) students. Background: There exists a positive correlation between technology related access, technology related competence, and academic outcomes. An increasing emphasis on expanding online education at the author’s institution, consistent with nationwide trends, means that it is unlikely that just an increase in online offerings alone will result in an improvement in the educational attainment of students, especially if such students lack access to technology and the technology related skills needed to take advantage of online learning. Most studies on levels of Technological Access and Technological Efficacy have dealt with either K-12 or minority populations with limited research on UNS and UNR populations who form the majority of students at the author’s institution. Methodology: This study used a cross-sectional survey research design to investigate the research questions. A web survey was sent to all students at the university except first semester new and first semester transfer students from various disciplines (n = 535). Descriptive and inferential statistics were used to analyze the survey data. Contribution: This research provides insight on a population (UNS and UNR) that is expanding in higher education. However, there is limited information related to levels of Technological Access and Technological Efficacy for this group. This paper is timely and relevant as adequate access to technology and technological competence is critical for success in the expanding field of online learning, and the research findings can be used to guide and inform subsequent actions vital to bridging any educational equity gap that might exist. Findings: A critical subset of the sample who were first generation, low income, and non-White (FGLINW) had significantly lower levels of Technological Access. In addition, nearly half of the survey sample used smartphones to access online courses. Technological Efficacy scores were significantly lower for students who dropped out of or never enrolled in an online course. Transfer students had significantly higher Technological Efficacy scores while independent students (determined by tax status for federal financial aid purposes) reflected higher Technological Efficacy, but at a marginally lower level of significance. Recommendations for Practitioners: Higher education administrators and educators should take into consideration the gaps in technology related access and skills to devise institutional interventions as well as formulate pedagogical approaches that account for such gaps in educational equity. This will help ensure pathways to sustained student success given the rapidly growing landscape of online education. Recommendation for Researchers: Similar studies need to be conducted in other institutions serving UNS and UNR students in order to bolster findings and increase awareness. Impact on Society: The digital divide with respect to Technological Access and Technological Efficacy that impacts UNS and UNR student populations must be addressed to better prepare such groups for both academic and subsequent professional success. Addressing such gaps will not only help disadvantaged students maximize their educational opportunities but will also prepare them to navigate the challenges of an increasingly technology driven society. Future Research: Given that it is more challenging to write papers and complete projects using a smartphone, is there a homework gap for UNS and UNR students that may impact their academic success? What is the impact of differing levels of Technological Efficacy on specific academic outcomes of UNS and UNR students?