Language learning is an emerging research area where researchers have done significant contributions by incorporating technological assistantship (i.e., computer- and mobile-assistant learning). However, it has been revealed from the recent empirical studies that little attention is given on grammar learning with the proper instructional materials design and the motivational framework for designing an efficient mobile-assisted grammar learning tool. This paper hence, reports a preliminary study that investigated learner motivation when a mobile-assisted tool for tense learning was used. This study applied the Attention-Relevance-Confidence-Satisfaction (ARCS) model. It was hypothesized that with the use of the designed mobile- assisted tense learning tool students would be motivated to learn grammar (English tense). In addition, with the increase of motivation, performance outcome in paper- based test would also be improved. With the purpose to investigate the impact of the tool, a sequential mixed-method research design was employed with the use of three research instruments; Instructional Materials Motivation Survey (IMMS), a paper-based test and an interview protocol using a semi-structured interview. Participants were 115 undergraduate students, who were enrolled in a remedial English course. The findings showed that with the effective design of instructional materials, students were motivated to learn grammar, where they were positive at improving their attitude towards learning (male 86%, female 80%). The IMMS findings revealed that students’ motivation increased after using the tool. Moreover, students improved their performance level that was revealed from the outcome of paper-based instrument. Therefore, it is confirmed that the study contributed to designing an effective multimedia based instructions for a mobile-assisted tool that increased learners’ motivational attitude which resulted in an improved learning performance.

The widespread adoption of technology-enhanced learning in various knowledge disciplines has pushed forward the development of information technology-assisted media for language learning and teaching. However, most of the existing electronic-learning (e-learning) solutions have underexplored and under-addressed given specific characteristics of grammar learning, which is one of the most demanding areas of language education. The lack of pedagogically-informed instructional design to enhance learning performance on the current system can result in low motivation and engagement due to imbalance and excessive increase of the cognitive load. This paper attempts to address these deficiencies posed by the existing systems by proposing smart communication networks that are driven by the student learning experience to manage cognitive load in the context of grammar learning. The e-grammar learning networks serve as a collaborative learning platform that combines a pedagogically-informed instructional model named Attention, Relevance, Confidence and Satisfaction (ARCS) and cyber interaction among teaching/learning agents. From the technological perspective, our numerical simulations demonstrate the desirable performance indicators of the proposed networks to facilitate information exchange and learning. From the education perspective, our empirical studies shows that the overall smart network-enabled egrammar learning system has desirable characteristics to motivate learners (m = 3.78) and manage their overall cognitive load (m=1.73), which suggest promising capability of the proposed system.

Recent years have seen continuous exposure of online social network (OSN) users to the security vulnerability due to socio-technical cyber hazards committed by OSN associates. Unfortunately, the current OSN platforms has lack of functionalities for automatic users protection in initiating online friendship and online users interaction within their circle. Moreover, the users cannot analyze their associates' time-varying and changing behavior, which is a strong indicator of malicious activities. To address this issue, this paper proposes a design of automatic two-phase pre- and post-filtering approach with the objective of mitigating the cyber threats in OSN platforms. In the pre-filtering phase, we propose an method where each user can access a reliable technique to select a friend after getting his/her details authenticity. In the post-filtering phase, a control mechanism is established to recognize malicious posts/actions of the associates in order to limit the spread of the corresponding cyber threats. Our empirical analysis shows some significant comparison data towards hybrid (combined pre- and post-filtering) method, where the users' consents for our proposed method exceed significantly over the other competing techniques. In fact, the proportional mean value of the hybrid method is increased almost twice (2.03) of the existing methods.

The proliferation of modern mobile technologies on grammar learning (i.e., m-grammar learning) has generated a multitude of challenges in developing effective pedagogically-informed learning tools. The existing systems have mostly suffered from low motivation and poor learning effectiveness because of the three key reasons, namely: i) a weak tie to motivational theoretical principles, ii) a lack of proper instructional design, and iii) a lack of proper infrastructural design for data sharing between students and instructors. To deal with this issue, this paper presents MATT: a Mobile-Assisted Tense Tool that encapsulates an m-grammar instructional design leveraging upon cloud-fog-edge collaborative networking. Central to MATT is the incorporation of the Cognitive Theory of Multimedia Learning principles to minimize the extraneous cognitive load and a motivational model to increase motivation and learning effectiveness. To ensure effective instructional design, we exploit adaptive and dynamic approaches embodied in a flexible instructional paradigm that takes advantage of collective learning data exchange across cloud (central unit), fog (regional units) and edge (end devices/learners). To demonstrate the overall effectiveness of this system, we describe our findings in the evaluation of both the learning aspect (using a quantitative research design) and collaborative network performance (using numerical simulation). With an appropriate condition of delay-tolerant network-enabled learning data exchange, the results suggest that the students’ cognitive load is low and motivational nature is high after using this system, which led them to perform more positively in the post-test evaluation.

Human factors remain the dominant contributor to cybersecurity incidents, yet awareness training produces only moderate and often non-durable behaviour change, and most evaluated programs are either purely digital or evaluated only at the framework level. This study addresses two gaps: the scarcity of empirical and demographically stratified evidence for multi-modal community-facing awareness programs, and the lack of an explicit account of how artificial intelligence (AI) should be integrated into such programs rather than treated as an optional add-on. We evaluate Cyber4Me, a four-stage individual-awareness intervention (community roadshows, structured training, a hackathon, and a physical–digital escape room) that is wrapped in a cross-cutting AI adaptive layer built entirely on structured performance and behaviour data baseline competency tiering, awareness–behaviour gap detection, predictive early-warning, and personalised recommendation, with no reliance on free text. Using a single-group pre–post design with 130 participants in the UK Black Country region and a multi-dimensional Likert instrument, all four competency domains (confidence, familiarity, GDPR knowledge, incident-response preparedness) improved significantly (paired-t, all $p<0.001$; large within-participant effects, Cohen’s $d\ge 1.0$). Improvement was strongly moderated by demographics: older adults gained most in familiarity, undergraduates in confidence, and lower-education participants in regulatory knowledge. The contributions are as follows: transparent and demographically stratified pre–post evidence for a multi-modal awareness program with effect sizes reported; a fitness-for-purpose comparison against contemporary analogs (KnowBe4, Proofpoint, CyberPatriot, iCAT, CAT-RWE, GPT-CSAT, escape-room studies) that treats AI as a first-class design dimension; and an articulated AI integration architecture for the framework, demonstrated offline on the cohort using only structured performance and behaviour data (no free text). In this architecture, a gradient-boosted classifier assigns participants to three baseline competency tiers at 93.1% cross-validated accuracy; these tiers differ sharply in measured improvement (ANOVA $F=68.8$, $p<0.001$; Foundational $+1.79$ vs. Applied $+0.30$ scale points), an awareness–behaviour gap segment is detected and predicted from intake signals alone (AUC $=0.73$), and a recommender routes participants to personalised follow-on tracks. As the design is single-group and self-reported, results are reported as evidence of within-participant change associated with the intervention rather than as a causal efficacy estimate, and the AI layer is demonstrated for feasibility rather than being evaluated as a separate trial arm; the scope is explicitly individual security awareness and behaviour, not technical network, IIoT, or cloud security.