Forging a learner-centric blended-learning framework via an adaptive content-based architecture

Abstract

The covid-19 pandemic was reported with significant negative impact on global education with shocks that disrupted the learning processes via the closure of traditional classrooms/schools from 2020 to March 2022. These effects have continued to ripple across even with advances in media literacy. The Nigerian frontier has also witnessed a paradigm shift in the adoption/integration of the information and communication tech as tools for both digital revolution and advancement of alternative education delivery. Today’s education which aspires for growth and progressive development is assured of positive changes if priority for educational values and ICT is harnessed. Past educational theories seem not to cope with the ever-changing, information society. Nigeria must develop strategies to address education reforms with frameworks to bridge these gaps vid post covid-19 era. Our study implements a hybrid a(synchronous) learning framework for Nigerian Tertiary education. Result shows improved learner cognition, engaged qualitative learning, and a learning scenario that ensures a power shift in the educational structure that will further equip learners to become knowledge producer, help teachers to emancipate students academically, in a framework that measures quality of engaged student’s learning

StreamBoostE: A Hybrid Boosting-Collaborative Filter Scheme for Adaptive User-Item Recommender for Streaming Services

“…The rating prediction was computed on an unrated user item obtained from the active user's neighbors K items. In rating prediction, it wanted to forecast the implicit and interacting information elements simultaneously [41]. It can be measured by the degree of resemblance between users.…”

“…Malasowe et al [33] extended Ojugo et al [41] by investigating the use of collaborative filtering technique to recommend an adaptive personalized learning schemes, which provided users with a learner-centered educational resources tailored to a specific student learning style; And in turn yielded improved academic goals outcome with personalized subject interests and topics. It agreed with [85] that results provided insight to facilitate personalized learning experiences as well as in formative testing skills [86] as was requisite for blended-learning approach [87] for adoption explosion during covid-19 era [88]- [91].…”

“…Machine learning (ML) models learns these patterns via features of interest, which helps them identify these patterns as signature classification that deviates from the norm [40]. A variety of ML have yielded resultant success with its adoption in collaborative filtering algorithm to include: Logistic Regression [41]- [43], Deep Learning [44]- [46], Bayesian model [47]- [49], Support Vector Machine [50]- [52], Random Forest [53]- [55], K-Nearest Neighbors [56]- [58], and in other models [59]- [61]. Their flexibility and performance is greatly hampered/degraded with the adopted choice in feature selection technique and data-preprocessing scheme [62], [63].…”

NiCuSBlockIoT: Sensor-based Cargo Assets Management and Traceability Blockchain Support for Nigerian Custom Services

“…Evidence suggests that the likelihood of repeat pandemic has increased in the past century [14], [43], [44]. These have been attributed to the increased (im)migration [45], global integration [46], urbanization [47], technological advances [48]- [50], land-use changes [51]- [53], and exploitation of natural environment vis-à-vis its resources [54]- [56]. These trends will likely continue to intensify and significant attention with well-formulated policies be enforced on the need to identify and limit emerging outbreaks, to expand and sustain investment to build up preparedness and health capacity to handle future occurrence [57]- [59].…”

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“…The system was also found to be secure and able to withstand physical stress -even with external influence [128]- [130]. Customer experience and convenience was evaluated by assessing the ease of usage and its integration into the existing model vis-à-vis its web app [131]- [133]. The system was easy to use, with no special training required.…”