A systematic review of technology-enhanced L2 listening development since 2000

Since 2000, technology-enhanced L2 listening development (TELD) has been increasingly investigated. However, systematic reviews concerning the technologies, learning tasks, and outcomes of TELD remain limited. To fill this gap, we conducted a systematic review of publications from 2000 to 2022 on TELD from the perspectives of technologies, learning tasks, and learning outcomes. Forty-six articles from Web of Science were screened by predefined criteria and analysed based on a step-by-step procedure using the PRISMA framework. The findings revealed 13 types of technology and 19 learning tasks useful for TELD. TELD was effective both in terms of building listening skills and enhancing learner emotions. The studies showed that TELD supported learner interactions, encouraged active engagement, and augmented various learning tasks. Based on the findings, we developed a TELD model consisting of two parts: “Within cognitive systems,” in which learners deal with cognitive schemata, listening strategy application, and listening practice via solid attention; “outside of cognitive systems,” in which TELD can construct and reconstruct cognitive schemata, support listening practices, encourage and guide listening strategy application, and improve learner emotions and attention by providing learning materials and activities based on listening-related knowledge, listening exercises with feedback, prompts and feedback on listening strategy application, and a sense of enjoyment and comfort

Using Mobile Technology to Improve Autonomy in Students with Intellectual Disabilities in Postsecondary Education Programs

Nationwide there are approximately 200 postsecondary education programs that provide inclusive college experiences for young adults with intellectual disabilities (ID) (Grigal & Hart, 2010). To navigate college campuses, the greater surrounding community, and ultimately competitive employment, young adults with ID need literacy, communication, and navigation skills. The purpose of these two studies was to investigate the effects of mobile technology to improve the autonomy of students with ID enrolled in a postsecondary education program. The purpose of experiment I was to examine the effectiveness of three different communication applications (i.e., text, audio, and video) to send and receive text messages (i.e., iMessage, Heytell, and Tango) for college-aged students with ID. Four students enrolled in a PSE program at a large university in the Southeastern United States participated in experiment I. An alternating treatments design was used to examine if there were differences in the acquisition and communicative understanding of each application. The results indicated that each participant learned how to send and receive text messages using multiple applications. Furthermore, all students improved the quality of communication including grammar and mechanics, relevance and comprehension, and professionalism. Experiment II examined the effectiveness of a navigation application for three college-aged students with ID also enrolled in a PSE program. Using a withdrawal/reversal ABAB design, students used the Apple iPhone and the Heads Up Navigator application to navigate to novel locations independently. First, students were given a copy of the university map during the baseline phase to walk to an unfamiliar location on campus. During the mobile application phase, students were taught how to operate and use a mobile device and navigation application (i.e., Heads Up Navigator) to navigate to unfamiliar places. Results from Experiment II indicated all students improved navigation skills with 100% nonoverlapping data which indicated a highly effective intervention. Visual analysis procedures were used to evaluate the intervention effects of both studies. Findings from the studies include implications for PSE and adult participants, the viability of mobile technology as an effective tool, and using digital tools to teach leisure and work skills. Recommendations for future research and practice are discussed

Engaging undergraduate students in an online science course: the relationship between instructor prompt and student engagement in synchronous class sessions

The number of online courses in higher education is on the rise; however, empirical evidence elucidating best practices for synchronous online instruction is needed to implement these courses. The purpose of this dissertation was to perform a mixed-method investigation into the relationships between instructor prompt and student engagement in 5 areas based on the 7 Principles of Good Practices in Undergraduate Education using recorded chat, video, and audio transcripts of two recent fully online nutrition courses. A total of 25 previously recorded synchronous sessions including oral and textual chat interactions were transcribed. Every line of student interaction was determined to be either superficial or containing evidence of at least one instance of engagement. Every line of instructor interaction was concurrently coded for at least one of the following forms of prompt: social, organizational, intellectual. Inter-tester reliability of coded interactions was determined to be excellent (Cohen\u27s kappa = 0.91) on a 5% sample of the entire dataset before comprehensive analysis continued. In total, 172,380 words were exchanged through 13,394 oral and text interactions across all class sessions. With 54% of student interactions deemed superficial the remainder produced a total of 8,906 student engagements. There were 4,125 instructor prompts composed of 48% intellectual, 30% organizational, and 22% social cues. Pearson correlations were performed to investigate relationships between prompt and engagement across class sessions. Intellectual prompts were the best predictor of faculty interactions, active and collaborative learning, and academic challenge (r=0.77, r=0.78, r=0.54 respectively); organizational prompts were the best predictor of enriching academic experiences (r=0.72); and social prompts were the best predictor of supportive campus environments (r=0.79) with all of these being significant (p\u3c0.01). No category of engagement was significantly correlated to class performance. Online synchronous class sessions can promote high levels of student engagement. A variety of instructor prompts must be used in order to promote student engagement across a number of different categories. Finally, care should be taken in order to craft and facilitate learning activities in synchronous online class sessions in order to achieve desired learning outcomes

Mobile information communication technology for crisis management : understanding user behavior, response and training

SMS text-messaging is an interoperable communication vehicle known to be dependable for mass media alert notifications in crisis management. SMS text-messaging also offers potential as one viable two-way communication alternative for field responders in crisis response. Both continuously changing mobile information communication technologies and the importance of precise information exchange constitute a need for communication protocol training and practice. This study introduces a technology-mediated training technique based on speech act and communicative action theories. These theories are used to inform the design of a baseline measure for task performance improvement and to suggest a model to predict communication readiness. Because this research bridges two fields - information systems and communication - it provides a model for full construct-representation of text-based interaction in a technology-mediated environment. The proposed model is validated through a web-based training application with 50 participants who have different crisis response backgrounds, including emergency management practitioners, first responders, public safety volunteers, community volunteers, community citizens, and students over the age of 18. Each group encompasses diverse technological skill and usage levels. The web-based training application developed in the present study features plain language training so that a clear understanding of user behavior, response, and training would emerge. The training and crisis scenario are rendered through multimedia recordings and designed to measure task response, based on the 160 character per SMS text-message exchange limit. The mixed-methods design begins with a crisis scenario, followed by pre-training measures, three repeated training measures, and concludes with post-training measures. A total of six tasks are introduced (3 pre-training and 3 post-training) in which each participant interfaces with the web-based training application through a high-speed Internet connection. Task response level results show promise for this exploratory research and contribute to a new discourse mode that extends to mobile technology penetration. Future research will focus on refinement of the model\u27s task performance measures and will seek to introduce additional situation-based scenarios and mixed-modes of communication. During this next research phase, the objective is to incorporate the model into mobile device usage and operationalize the model in authentic crisis management contexts. If successful in extended field simulation, the model may have the potential to ensure effective mobile information communication within the context of crisis

Evaluation of feedback and a graphical game element in an SMS intervention to increase attendance among at-risk high school students

Mobile phone text messages have been used to deliver interventions that support positive behaviour in many health contexts. School absenteeism is a problem across the world leading to reduced reading and writing skills and increased likelihood of school dropout. Much of the published research in both the fields of school absenteeism interventions and text message driven behavioural change is not based on theoretical foundations and so it difficult to generalise findings from one study into another context. This thesis describes the development and evaluation of an intervention delivered by text and picture messages that supports reduced school absenteeism among at-risk youth. This thesis describes the intervention design process and its connections with Social Cognitive Theory, the Transtheoretical Model, Theory of Planned Behaviour and Self Determination Theory. A pilot trial was undertaken to evaluate the technical feasibility of the intervention method and findings informed the intervention and study design that was evaluated in two three-arm single blind pre-post randomised controlled trials. The studies compared the effect on absenteeism of two styles of intervention with a control group. The first sent feedback of recent attendance performance in the form of a graphical scoreboard and the second sent the same feedback together with individually tailored autonomy supporting messages based on recent attendance that offered praise and motivational content. When messages were sent immediately after school in Study One, rates of full day absenteeism were reduced by half when compared to the control group. When messages were sent at the time that students woke up to go to school in Study Two, there was no significant difference between the groups. While it was hypothesised that students receiving the autonomy supporting text in addition to the feedback image would have higher perceived autonomy in school and lower absenteeism rates, there was no significant mediating effect. Moderation analysis found that the effect of the feedback on absenteeism was especially strong among participants for whom the primary language spoken at home was Spanish rather than English. These findings further understandings related to the effect of autonomy supporting messages on perceived autonomy and the timing of feedback

Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models

Software development is an inherently collaborative process, where various stakeholders frequently express their opinions and emotions across diverse platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. Over the years, many tools have been proposed to aid in sentiment analysis, but accurately identifying the sentiments expressed in software engineering datasets remains challenging. Although fine-tuned smaller large language models (sLLMs) have shown potential in handling software engineering tasks, they struggle with the shortage of labeled data. With the emergence of bigger large language models (bLLMs), it is pertinent to investigate whether they can handle this challenge in the context of sentiment analysis for software engineering. In this work, we undertake a comprehensive empirical study using five established datasets. We assess the performance of three open-source bLLMs in both zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.Comment: Submitted to TOSE

Expert perspectives on using mainstream mobile technology for school-age children who require augmentative and alternative communcation (AAC): a Policy Delphi study

Despite legislation in the U.S.A requiring the use of assistive technology in special education, there remains an underutilization of technology-based speech intervention for young students who require augmentative and alternative communication (AAC). The purpose of this Policy Delphi study was to address three guiding research questions that relate to the feasibility of using mainstream mobile technology, facilitative actions, and stakeholder roles for implementation and utilization of AAC in elementary school settings. Data were collected in two rounds of questionnaires given to experts in special education, assistive technology and speech and language pathology, with experience in AAC. Round 1 included 19 participants, 14 of whom also completed the Round 2 questionnaire. The results indicated that a very strong case can be made that mainstream mobile devices have several advantages over traditional AAC systems, not only in their affordability, but also transparency and social acceptance by providing an ideal medium for inclusion in mainstream settings. A challenge that confronts AAC innovations is the tendency to focus on the technology instead of pedagogical, social and therapeutic goals. Until a perfect AAC system becomes available for mainstream mobile devices that meet individuals’ communicative, educational and physical needs and personal preferences, it is apparent that multimodality will continue to be the model. The utilization of mainstream mobile technology for AAC necessitates certain facilitative actions and stakeholder responsibilities. Team collaboration is essential in supporting AAC use and, when applicable, facilitating the inclusion and mainstreaming of students who use AAC in the general education setting

Identifying Privacy Policy in Service Terms Using Natural Language Processing

Ever since technology (tech) companies realized that people\u27s usage data from their activities on mobile applications to the internet could be sold to advertisers for a profit, it began the Big Data era where tech companies collect as much data as possible from users. One of the benefits of this new era is the creation of new types of jobs such as data scientists, Big Data engineers, etc. However, this new era has also raised one of the hottest topics, which is data privacy. A myriad number of complaints have been raised on data privacy, such as how much access most mobile applications require to function correctly, from having access to a user\u27s contact list to media files. Furthermore, the level of tracking has reached new heights, from tracking mobile phone location, activities on search engines, to phone battery life percentage. However much data is collected, it is within the tech companies\u27 right to collect the data because they provide a privacy policy that informs the user on the type of data they collect, how they use that data, and how they share that data. In addition, we find that all privacy policies used in this research state that by using their mobile application, the user agrees to their terms and conditions. Most alarmingly, research done on privacy policies has found that only 9% of mobile app users read legal terms and conditions [2] because they are too long, which is a worryingly low number. Therefore, in this thesis, we present two summarization programs that take in privacy policy text as input and produce a shorter summarized version of the privacy policy. The results from the two summarization programs show that both implementations achieve an average of at least 50%, 90%, and 85% on the same sentence, clear sentence, and summary score grading metrics, respectively

Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMs

Understanding and identifying the causes behind developers' emotions (e.g., Frustration caused by `delays in merging pull requests') can be crucial towards finding solutions to problems and fostering collaboration in open-source communities. Effectively identifying such information in the high volume of communications across the different project channels, such as chats, emails, and issue comments, requires automated recognition of emotions and their causes. To enable this automation, large-scale software engineering-specific datasets that can be used to train accurate machine learning models are required. However, such datasets are expensive to create with the variety and informal nature of software projects' communication channels. In this paper, we explore zero-shot LLMs that are pre-trained on massive datasets but without being fine-tuned specifically for the task of detecting emotion causes in software engineering: ChatGPT, GPT-4, and flan-alpaca. Our evaluation indicates that these recently available models can identify emotion categories when given detailed emotions, although they perform worse than the top-rated models. For emotion cause identification, our results indicate that zero-shot LLMs are effective at recognizing the correct emotion cause with a BLEU-2 score of 0.598. To highlight the potential use of these techniques, we conduct a case study of the causes of Frustration in the last year of development of a popular open-source project, revealing several interesting insights