Several studies indicate the benefit of mapping gamification elements to personality. However, this mapping requires a strong understanding of the relationship between gamification elements and personality. The existing research that has tried to address this relationship is based on a self-report questionnaire that is obtained from only those learners who complete the entire study. Unfortunately, a bias may result from first forcing learners to complete an entire study and then ignoring learners who drop out in the middle of a study. To overcome this bias, we use a more objective approach to understand the relationship between personality and gamification. In our study, we use the dropout rate as a proxy for learner motivation. We hypothesize that learners who are more motivated by gamification elements will use the gamified website longer. Furthermore, because we use a different method than previous studies used, we analyse our data differently. Our solution is to use survival analysis to analyse our data, which confirms the benefit of using gamification to enhance learner motivation. Our results point to the relationship between the response of different personalities and gamification elements. In further studies, we recommend to use this same approach but with more gamification elements. RESEARCH HIGHLIGHTS Gamification plays an important role in enhancing learners’ motivation and engagement. Different personalities respond differently to gamification elements. The dropout rate can be used to measure learners’ motivation. Enhancing the motivation of learners does not necessarily improve their learning.

Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning and forecasting, and flood damage mitigation all benefit from rain forecasting. Prior to recent decades’ worth of research, this domain demonstrated to be promising prospects in time series prediction tasks. Therefore, the main aim of this study is to build a forecasting model based on the exponential smoothing-long-short term memory (ES-LSTM) structure and recurrent neural networks (RNNs) for predicting hourly precipitation seasons; and classify the precipitation using an artificial neural network (ANN) model and decision tree (DT) algorithm. We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the proposed model. The findings showed that the ES-LSTM and RNN had achieved 3.17 and 6.42 in terms of mean absolute percentage error (MAPE), respectively. Meanwhile, the ANN and DT models obtained a prediction accuracy rate of 96.65% and 84.0%, respectively. Finally, the outcomes revealed that ES-LSTM and ANN had achieved the best results compared to other models.

Natural language processing (NLP) is the art of investigating others’ positive and cooperative communication and rapprochement with others as well as the art of communicating and speaking with others. Furthermore, NLP techniques may substantially enhance most phases of the information-system lifecycle, facilitate access to information for users, and allow for new paradigms in the usage of information-system services. NLP also has an important role in designing the study, presenting two fields converging on one side and overlapping on the other, namely the field of the NAO-robot world and the field of education, technology, and progress. The selected articles classified the study into four categories: special needs, kindergartens, schools, and universities. Our study looked at accurate keyword research. They are artificial intelligence, learning and teaching, education, NAO robot, undergraduate students, and university. In two fields of twelve journals and citations on reliable/high-reputation scientific sites, 82 scientific articles were extracted. From the Scientific Journal Rankings (SJR) website, the study samples included twelve reliable/high-reputation scientific journals for the period from 2014 to 2023 from well-known scientific journals with a high impact factor. This study evaluated the effect of a systematic literature review of NAO educational robots on language programming. It aimed to be a platform and guide for researchers, interested persons, trainees, supervisors, students, and those interested in the fields of NAO robots and education. All studies recognized the superiority and progress of NAO robots in the educational field. They concluded by urging students to publish in highly influential journals with a high scientific impact within the two fields of study by focusing on the study-sample journals.

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