The development of methods for assessing lecturers' performance is needed to motivate lecturers to achieve institutional targets. Currently, lecturers are required to be able to adapt to the rapid development of technology. Lecturer performance assessment must be done periodically. Competence is measured as a basis for planning resource development activities. The method that is often used for assessing lecturer performance is the Simple Additive Weighting (SAW) method. However, the SAW method has drawbacks, namely 1) the process of determining criteria is only carried out by the leadership (subjective); 2) The SAW method can only be applied to multi-criteria data ; 3) Data ranking problems. Based on this deficiency, a new method was built, namely, the Weighted Performance Indicator (WPI) method using respondents’ opinion to determine the criteria. This study aims to compare the performance of the two methods. Testing criteria using SPPS application dan WPI method, while testing methods utilized the SAW method and the WPI method. The results of the criterion test show the Percentage of Similarity of data validity = 96.7 % witht the minimum percentage limit (MPL) = 40%. While the results of the SAW method and WPI method testing resulted in the highest score in the 13th alternative, namely SAW score (v13) = 793.76 and WP score (WP13) = 0.928, and the lowest value in the 30th alternative, SAW score (v30) = 18.60 and WP score (WP30) = 0.140. the ranking positions in these two methods show similarities. However, for other alternatives, the rating value can be different. 
The WPI method is a scientific development in the field of decision support systems that can be applied to other performance assessments, such other human resources, system performance assesment etc. 
The results of this study prove that the WPI method can be used as a performance assessment method with different characteristics from the SAW method.

Education plays a significant role in individuals’ development and economic growth of the developing coun-tries like India. Dropout of students from their studies is the major concern for any order of education. Some models for predicting the dropout of students are developed with several factors. Many of them lacked consistencies as they backed their studies with the academic performance of the students. Especially, for those students suffered with physical impairment the drop out depends on several external factors. Students drop out of school for a variety of reasons, including financial difficulties, parents' unwillingness, distance and a lack of basic amenities, poor educational quality, an inadequate school environment and building, overcrowded classrooms, improper languages of instruction, carelessness on the part of teachers, and security issues in girls' schools. Hence, this work proposes a novel HFIPO-DPNN to predicting the physically handicapped student’s dropout from School also to predict the student dropout rooted on the previous semester marks. The proposed model enclosed the hybrid firefly and improved particle swarm algorithm to optimize the feature selection that influence the dropout of hearing-impaired students. The optimized feature data are used to predict the dropout with the novel DPNN. The optimized data was split and used for training the DPNN. The testing data is used to evaluate the performance of the proposed framework. The outcome for the proposed framework is evaluated on several metrics. The accuracy of the proposed model is about 99.02%. The HFIPO-DPNN framework can be enhanced for predicting the dropout for students with other disabilities. The optimization revealed that factors other than family factors should be taken into account when predicting dropout.

Morocco has recently introduced the syllable-based reading approach (SbA) in Arabic and French Curricula. Thus, this study aimed at measuring the impact and effectiveness of said approach on Moroccan pupils’ Arabic reading competency as well as how teachers approach it based on the hermeneutic phenomenology theoretical framework. A quantitative survey questionnaire was distributed to 227 1st and 2nd grade teachers from all twelve regions of Morocco. The Statistical Package for the Social Sciences (SPSS) was used to analyze data through descriptive statistics and statistical hypothesis testing. The results showed that most Moroccan teachers advocate the use of the SbA as it positively impacts Arabic reading competency. They have, also, pointed out that time and demanding lesson planning are some of the SbA’s drawbacks. Also, it was found that there are no differences in SbA’s teaching practices and perceptions between female and male teachers except for the practice of assigning reading homework as female teachers tend to assign more reading homework. The results of the study are of significant importance to the MENA region as it evaluates field work regarding SbA.

Learning practices commenced to shift from face-to-face offline class learning to online classes with technological networks specifically on sudden COVID-19 crises. . This sort of variation in their learning method sparks question about students' perception of the new learning system. The objective of the study was to compare English language learning, between online classes and Offline-classes and it explicates different students' perceptions of such learning practices regarding the benefits, improvements, and drawbacks of online and offline modes. The research approach of study, proceeds with a quantitative study, using statistical analysis through questionnaire distribution. The participants of the study were the school students, obtained from Government and private schools in Telangana. The quality of the study stands outstanding in addressing the effectiveness of blended learning both online and offline learning and aids to study nature of the approach if integration of learning modes including face-to face and online learning incorporated and the consideration to improvise qualities learning experiences of students. With those aspects, the research is significant to prove the preference of students to elucidate that offline classroom learning is more preferable than online English learning. The value of the research is recognised that it aids the educators, leadership authorities and researchers to understand parameters leading to efficient learning practices, enhanced collaborative student performance outcomes assisting to select the appropriate technologies in case of any pandemic crisis and to inhibit collaborative learning in and out of classroom.  The most general obstacles faced by students in online English learning are materials insufficiency, lack of communicative skills training, lacking reading activities participation, absence of interaction, the inability of queries or doubts clarification, and exercise exposure are addressed by the analysis outcomes. The comparative perception outcomes explicated that Offline English language learning stands out as more efficient than the online learning method. 

Previously acquired knowledge plays a significant role to learn new knowledge and skills.  Previously acquired knowledge consists of Short-term memory and Long-term memory.  Though it is a well-accepted learning phenomenon, it is challenging to empirically analyse the impact of prior knowledge on learning. In this paper, we use two systems models for human thinking proposed by Nobel Laureate Prof. Daniel Kahneman. This is a  model for human cognition which uses two systems of thinking—the first being quick and intuitively known as fast thinking and the second being slow and tedious known as slow thinking. While slow thinking uses long-term memory, fast thinking uses short-term memory.  The impact of prior knowledge of programming language is analyzed to learn a new programming language. We assigned a learning task to two different groups with one having learnt a programming language i.e. senior students and the second group without any prior knowledge of programming language i.e. freshers. The impact of prior knowledge is measured and compared against the time taken to answer quizzes.

A method of choosing swarm optimization algorithms and using swarm intelligence for solving a certain class of optimization tasks in industry-specific geographic information systems was developed considering the stationarity characteristic of such systems. The method consists of 8 stages. Classes of swarm algorithms were studied. It is shown which classes of swarm algorithms should be used depending on the stationarity, quasi-stationarity or dynamics of the task solved by an industry geographic information system. An information model of geodata that consists in a formalized combination of their spatial and attributive components, which allows considering the relational, semantic and frame models of knowledge representation of the attributive component, was developed. A method of choosing optimization methods designed to work as part of a decision support system within an industry-specific geographic information system was developed. It includes conceptual information modeling, optimization criteria selection, and objective function analysis and modeling. This method allows choosing the most suitable swarm optimization method (or a set of methods).