Aim/Purpose: This article aimed to design and evaluate a pedagogical technique for altering students’ classroom digital multitasking behaviors. The technique we designed and evaluated is called course-based undergraduate research experience (CURE). With this technique, the students wrote a research article based on a multitasking experiment that the instructor conducted with the students. The students conducted a literature review, developed their own research questions, they analyzed experiment data, and presented results. This study evaluated the how the CURE contributed to student multitasking behavior change. Background: Multitasking is defined as doing more than one thing at a time. Multitasking is really the engagement in individual and discrete tasks that are performed in succession. Research showed that students multitasked very often during courses. Researchers indicated that this was a problem especially for online teaching, because when students went online, they tended to multitask. Extant research indicated that digital multitasking in class harmed student performance. Multiple studies suggested that students who multitasked spent more time finishing their tasks and made more mistakes. Regardless of students’ gender or GPA, students who multitasked in class performed worse and got a lower grade than those who did not. However, little is known about how to change students’ digital multitasking behaviors. In this study, we used the transtheoretical model of behavior change to investigate how our pedagogical technique (CURE) changed students’ digital multitasking behaviors. Methodology: Using a course-based undergraduate research experience design, a new classroom intervention was designed and evaluated through a content analysis of pre- and post-intervention student reflections. As part of the course-based undergraduate research experience design, the students conducted a literature review, developed their own research questions, they analyzed experiment data, and presented results. This study evaluated the how teaching using a course-based undergraduate research experience contributed to student multitasking behavior change. Transtheoretical model of behavior change was used to investigate how our pedagogical technique changed students’ digital multitasking behaviors. Contribution: The paper described how teaching using a course-based undergraduate research experience can be used in practice. Further, it demonstrated the utility of this technique in changing student digital multitasking behaviors. This study contributed to constructivist approaches in education. Other unwanted student attitudes and behaviors can be changed using this approach to learning. Findings: As a result of CURE teaching, a majority of students observed the negative aspects of multitasking and intended to change their digital multitasking behaviors. Sixty-one percent of the participants experienced attitude changes, namely increased negative attitude towards multitasking in class. This is important because research found that while both students and instructors believed off-task technology use hinders learning, their views differed significantly, with more instructors than students feeling strongly that students’ use of technology in class is a problem. Moreover, our study showed that with teaching using CURE, it is possible to move the students on the ladder of change as quickly as within one semester (13 weeks). Seventy-one percent of the students reported moving to a higher stage of change post-intervention. Recommendations for Practitioners: Faculty wishing to curb student digital multitasking behaviors may conduct in-class experimentation with multitasking and have their students write a research report on their findings. Course-based undergraduate research experiences may make the effects of digital multitasking more apparent to the students. The students may become more aware of their own multitasking behaviors rather than doing them habitually. This technique is also recommended for those instructors who would like to introduce academic careers as a potential career option to their students. Recommendation for Researchers: Researchers should explore changing other unwanted undergraduate student behaviors with course-based undergraduate experiences. Researchers may use the transtheoretical model of change to evaluate the effectiveness of techniques used to change behaviors. Impact on Society: The negative outcomes of digital multitasking are not confined to the classroom. Digital multitasking impacts productivity in many domains. If techniques such as those used in this article become more common, changes in multitasking intentions could show broad improvements in productivity across many fields. Future Research: This paper constitutes a pilot study due to the small convenience sample that is used for the study. Future research should replicate this study with larger and randomized samples. Further investigation of the CURE technique can improve its effectiveness or reduce the instructor input while attaining the same behavioral changes.

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?

Aim/Purpose: The purpose of the current study is to introduce a digital thinking skills (DTS) theoretical model (DTSM) that could support and enhance digital instruction best practices in schools. Methodology: We have taken a mixed-methods approach. Our respondents represent diverse cultural, linguistic, pedagogical, and social heritages. Contribution: The study provides a theoretical model developed by Eshet-Alkalai and Aviram that could impact subsequent digital teaching in schools. The highly accessible model may help teachers understand the cognitive learning outcomes that derive from frequently used digital tools. Findings: We found that teachers do not have a pedagogical concept of digital thinking skills, though many believe such skills might have a positive effect on their learners’ achievements. School culture plays a key role in effective DI delivery. Teachers want better in-service IT instruction. Recommendations for Practitioners: When distance learning has become the order of the day, we recommend practitioners connect pedagogical methodology and disciplinary content with new technology to boost learning outcomes. Recent world events have shown that in many cases practitioners have not been exposed to multiple digital options, especially those that not only present and review learning content but also boost the creation and dissemination of new knowledge. Recommendation for Researchers: We recommend researchers review different types of available resources and their effective implementation in the school curricula in order to foster creativity and more profound thinking among teachers and learners. Impact on Society: Better informed and greatly improved DI in schools is clearly crucial for twenty-first-century systems. As we go to press, in the middle of the coronavirus world-wide lockdown, these words resonate more than ever before. Our research both highlights this obvious need and provides a heuristic bridge between basic research and classrooms. Future Research: Future studies should focus on a pedagogical digital model that can enhance DI best practices in schools.

Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student’s grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence learning processes, with and without a general overview of the problem that it is under examination. Applying the idea of the general perspective that the network gets on the sentences and deriving recommendations from this for teaching processes. Background: We looked for common patterns of computer errors and human grammar mistakes. Also deducing the neural network’s learning process, deriving conclusions, and applying concepts from this process to the process of human learning. Methodology: We used DL technologies and research methods. After analysis, we built models from three types of complex neuronal networks – LSTM, Bi-LSTM, and GRU – with sequence-to-sequence architecture. After this, we combined the sequence-to- sequence architecture model with the attention mechanism that gives a general overview of the input that the network receives. Contribution: The cost of computer applications is cheaper than that of manual human effort, and the availability of a computer program is much greater than that of humans to perform the same task. Thus, using computer applications, we can get many desired examples of mistakes without having to pay humans to perform the same task. Understanding the mistakes of the machine can help us to under-stand the human mistakes, because the human brain is the model of the artificial neural network. This way, we can facilitate the student learning process by teaching students not to make mistakes that we have seen made by the artificial neural network. We hope that with the method we have developed, it will be easier for teachers to discover common mistakes in students’ work before starting to teach them. In addition, we show that a “general explanation” of the issue under study can help the teaching and learning process. Findings: We performed the test case on the Hebrew language. From the mistakes we received from the computerized neuronal networks model we built, we were able to classify common human errors. That is, we were able to find a correspondence between machine mistakes and student mistakes. Recommendations for Practitioners: Use an artificial neural network to discover mistakes, and teach students not to make those mistakes. We recommend that before the teacher begins teaching a new topic, he or she gives a general explanation of the problems this topic deals with, and how to solve them. Recommendations for Researchers: To use machines that simulate the learning processes of the human brain, and study if we can thus learn about human learning processes. Impact on Society: When the computer makes the same mistakes as a human would, it is very easy to learn from those mistakes and improve the study process. The fact that ma-chine and humans make similar mistakes is a valuable insight, especially in the field of education, Since we can generate and analyze computer system errors instead of doing a survey of humans (who make mistakes similar to those of the machine); the teaching process becomes cheaper and more efficient. Future Research: We plan to create an automatic grammar-mistakes maker (for instance, by giving the artificial neural network only a tiny data-set to learn from) and ask the students to correct the errors made. In this way, the students will practice on the material in a focused manner. We plan to apply these techniques to other education subfields and, also, to non-educational fields. As far as we know, this is the first study to go in this direction ‒ instead of looking at organisms and building machines, to look at machines and learn about organisms.