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Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by 13% over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

The cognitive radio relay plays a vital role in cognitive radio networking (CRN), as it can improve the cognitive sum rate, extend the coverage, and improve the spectral efficiency. However, cognitive relay aided CRNs cannot obtain a maximal sum rate, when the existing sensing approach is applied to a CRN. In this paper, we present an enhanced sum rate in the cluster based cognitive radio relay network utilizing a reporting framework in the sequential approach. In this approach a secondary user (SU) extends its sensing time until right before the beginning of its reporting time slot by utilizing the reporting framework. Secondly all the individual measurement results from each relay aided SU are passed on to the corresponding cluster head (CH) through a noisy reporting channel, while the CH with a soft-fusion report is forwarded to the fusion center that provides the final decision using the n-out-of-k-rule. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained than with a conventional non-sequential approach, therefore making it applicable for the future Internet of Things. In addition, the sum rate of the primary network and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the n-out-of-k rule. By simulation, we show that the proposed sequential approach with a relay (Lemma 2 ) provides a significant sum rate gain compared to the conventional non-sequential approach with no relay (Lemma 1 ) under any condition.

The design of a water distribution network (WDN) is an ever-challenging problem. The formulation and application of optimization techniques for WDN design have been an important area of research. Recently, the introduction of chaos theory-based evolutionary algorithms (EAs), in addition to traditional random-based ones, has elevated the scope for further improving the performance of EAs. The present study proposes a chaos-directed genetic algorithm (CDGA) by incorporating chaos ergodicity in GA mechanics for the optimal design of WDNs by introducing two novel frameworks, namely non-sequential approach and sequential approach. In improving the search efficacy of GA, the influence of chaotic systems with high-dimensionality maps is also explored when compared to the low-dimensionality maps. Considering four widely studied WDN benchmark problems ranging from 8 to 454 dimensions, the performance of the proposed GA and CDGA models is evaluated. The results show that the CDGA models outperform GA with better search efficacy, requiring fewer function evaluations to locate the optimal solution. In addition, the CDGA models are found to outperform other optimization techniques reported previously to handle these benchmark problems. Based on the results obtained, the study suggests the use of the chaotic system with other bio-inspired techniques to further improve their searchability and, thus, their computational efficiency.

Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence the learners' progress for self-regulated learning (SRL) and course achievements. However, learners tend to be unsure about how note-taking is performed using online learning materials and lack knowledge of effective strategies for SRL. Furthermore, few studies have investigated blended learners' sequential patterns of e-book note-taking for SRL. Thus, in this paper, an exploratory study was conducted in an undergraduate course that implemented the BL design. The learning task for the blended learners in the present study was to study the learning material using BookRoll, an e-book system, during in-class and out-of-class learning sessions. Lag sequential analysis of the e-book learning behavior data was conducted to identify the blended learners' sequential behaviors of e-book note-taking for the cognitive strategy use of SRL. Moreover, the difference between higher- and lower-achievement blended learners in terms of their sequential behaviors of e-book note-taking for SRL was revealed. This study can help educators provide evidence-based educational feedback to learners regarding the identified sequential patterns of e-book note-taking that can be applied as effective strategies for promoting the cognitive strategy use of SRL and improvement of course achievement in BL.

Inferring dominance hierarchies is key to quantifying social dynamics within animal groups. Observed dyadic agonistic interactions remain an important source of data for studying dominance hierarchies. As a result, numerous (statistical) approaches attempt to derive and characterize dominance hierarchies from dyadic interactions. However, most of them ignore the temporal component of these interactions. We introduce a novel model to characterize dominance hierarchies using a sequential pairwise relationship model called Animal Dominance Network (AniDomNet). This model is inspired by the Elo ranking model, yet relaxes several of the underlying assumptions and allows us to study the dynamics of hierarchy formation. While addressing certain shortcomings of the current sequential methods, AniDomNet also excels at predicting the outcome of future interactions. Moreover, we propose a social agony‐based approach to obtain a directed acyclic graph (DAG) that represents the dominance hierarchy according to a fitted model. AniDomNet is shown to be a useful tool to detect mistakes (such as identity switches) made during the observation process.

This paper reports on the progress of users through 16 Coursera courses taught by University of Pennsylvania faculty for the first time between June 2012 and July 2013. Using descriptive analyses, this study advances knowledge by considering two definitions of massive open online course (MOOC) users (registrants and starters), comparing two approaches to measuring student progress through a MOOC course (sequential versus user driven), and examining several measures of MOOC outcomes and milestones. The patterns of user progression found in this study may not describe current or future patterns given the continued evolution of MOOCs. Nonetheless, the findings provide a baseline for future studies.

This study investigated students' modeling progress and strategies in a problem-solving simulation game through content analysis, and through supervised and unsupervised lag sequential analysis (LSA). Multiple data sources, including self-report models and activity logs, were collected from 25 senior high school students. The results of the content analysis found that the problem-solving simulation game helped most of the students to reflectively play with the science problem and build a workable model to solve it. By using the supervised LSA, it was found that the students who successful solved the game frequently linked the game contexts with the physics terminologies, while those who did not solve the problem simply relied on the intuitive knowledge provided in the reference materials. Furthermore, the unsupervised LSA identified four activity patterns that were not noticed in the supervised LSA: the fragmented, reference material centered, reference material aided modeling, and modeling centered patterns. Each pattern has certain associations with certain problem-solving outcomes. The results of this study also shed light on the use of different analytics techniques. While the supervised LSA is particularly helpful for depicting a contrast of activity patterns between two specific student groups, the unsupervised LSA is able to identify hidden significant patterns which were not clearly distinguished in the pre-defined student groups. Researchers may find these analytics techniques useful for analyzing students' learning processes.

Examining the sequential patterns of self-regulated learning (SRL) behaviors is gaining popularity to understand students' performance differences. However, few studies have looked at the transition probabilities among different SRL behaviors. Moreover, there is a lack of research investigating the temporal structures of students' SRL behaviors (e.g., repetitiveness and predictability) and how they related to students' performance. In this study, 75 students from a top North American university were tasked to diagnose a virtual patient in an intelligent tutoring system. We used recurrence quantification analysis and sequential analysis to analyze the temporal structures and sequential patterns of students' SRL behaviors. We compared the differences between low and high performers. We found that low performers had more single, isolated recurrent behaviors in problem-solving, whereas the recurrent behaviors of high performers were more likely to be part of a behavioral sequence. High performers also demonstrated a higher transition probability across the three phases of SRL than low performers. In addition, high performers were unique in that their behavioral state transitions were cyclically sustained. This study provided researchers with theoretical insights regarding the cyclical nature of SRL. This study has also methodological contributions to the analysis of the temporal structures of SRL behaviors.

What is the appropriate unit of analysis for the study of classroom discourse? One common analytic strategy employs individual discourse moves, which are coded, counted and used as indicators of the quality of classroom talk. In this article we question this practice, arguing that discourse moves are positioned within sequences that critically shape their meaning and effect. We illustrate this theoretical claim through exploration of a corpus of over 7000 discourse moves in primary literacy lessons. First, we use conventional measures such as the proportion of open and closed questions, and show how these indicators can be misleading when abstracted from the sequences in which they are embedded. We propose a complementary method, lag sequential analysis, which examines how discourse is sequentially structured—i.e. which discourse moves are followed by which other moves, and which chains of moves occur more frequently than expected by chance. We illustrate this method through re-analysis of our corpus of literacy lessons, examining differences between the sequential patterns found in the different classrooms observed. While lag sequential analysis does not resolve all problems inherent in systematic observation of classroom discourse, it does shed light on critical patterns in the data-set that would have otherwise gone unnoticed.

The retention of underrepresented students remains a significant challenge in the STEM (Science, Technology, Engineering and Math) disciplines. A broad range of studies across several disciplines have shown that conventional approaches to STEM instruction may have been unintentionally exclusive to students whose ethnicities are not traditionally represented in the STEM fields. This ‘exclusive’ classroom atmosphere has emerged as a major reason for the attrition of underrepresented minority students from STEM majors. In this manuscript, I describe a conceptual model called Deep Teaching, describing how pedagogical transformation incorporating practices that are more inclusive can occur. The model marks an evolution from other frameworks advancing inclusive instruction in higher education by advocating for the primacy of Freirean philosophy when thinking about self and student. Using specific examples, I discuss how a sequential approach to understanding ourselves and empathizing with students puts the instructor in a better position to create enduring, positive classroom climates. I also describe considerations necessary for various contexts, and suggestions for continued commitment to inclusive pedagogy in the long-term.