The study investigated the perception of teachers of agriculture about Supervised Agricultural Experience Programmes (SAEP) in secondary schools in Ekiti and Ondo States. The population used for the study consisted of 520 teachers of agricultural science in all the secondary schools in Ekiti and Ondo States. The sample used for this study was 136 teachers of agricultural science drawn through a proportionate stratified sampling technique to pick four(4) teachers from each of the 34 Local Government of the two states. The Instrument used was a structured questionnaire to investigate the extend to which the teachers agreed on disagree with statemenst regarding SAEP. The questionnaire were test and re-tested yielding a reliability co-efficient of 89. (Cronbach alpha). The data for the study were analyzed using mean, standard deviation t-test and two tailed probability statistics. The probability level was set at P<0.05. Thirty eight items were generated for the study. The study found out among others that the teaching of agriculture needs improvement; that though SEAP related contents are in the agricultural science curriculum the teaching and learning of agriculture are not vocationally oriented in Ekiti and Ondo States. It was recommended among others that agricultural programmes in all schools should include supervised agricultural experience programmes, while the State School Boards in collaboration with relevant Ministries should conduct informational workshop and orientation on SAEP for educational planners, administrator and teachers of agriculture in Nigeria.

The implementation of Lesson Study (LS) varies considerably across countries and institutions and is still in a phase of adaptation and experimentation. This article explains the result and the process of a school-based initiative endeavor to implement LS at a suburban elementary in Padang, Indonesia. The study involved 13 teachers, the principal and 6 classes of students. The data were collected through observation and interview. They were classified on the basis of three noticeable emerging themes- teacher collaboration, scaffolding, and reflection. The data were analyzed qualitatively. The results of data analysis reveal a promising improvement in these aspects. Implementing school- support LS increased by weaving the concept into practice helped teachers develop their professionalism gradually. It was obvious that the teachers felt more at ease to work collaboratively when they designed the lesson. This also affected their design which showed more meaningful learning activities and challenging tasks. Then, the teachers improved the way they scaffolded the pupils. The content of reflection and the way the results of reflection were conveyed became better. The principal’s support and the teachers’ strong willingness to elevate their quality apparently took an important role. In spite of that, there were some challenges in carrying out collaboration, providing appropriate scaffolding, and doing reflection. Changing the teachers’ common practice to LS apparently needs some adjustment and time.

The primary goal of this study is to explore what makes teachers accept Google Classroom (GCR). GCR platform is an emerging technology that could support online learning activities by offering outstanding benefits such as usability, flexibility, and task adaptability. Many of the students in Indonesia have al-ready used the GCR platform since the government has tried to provide it as a free online learning tool to support learning activities during the pandemic. However, there is limited understanding of users' behavior, especially Indonesian students' acceptance of the GCR platform. The model is tested by administering the online questionnaire to 261 university students in Indonesia. The extended Unified Theory of Acceptance and Use of Technology Model (UTAUT) model has been applied to observe users’ acceptance of GCR. The result Performance expectancy (PE), Effort expectancy (EE) Social Influence (SI), Facilitating Conditions (FC), Trust of Internet (TI) and Trust of Government (TG) considerably affected users’ intention to use the GCR. Moreover, Trust of Internet (TI) and Trust of Government (TG) also knowingly impacted Performance expectancy (PE).

The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one, being: type of high school, number of semesters taken, grade-point average, lockouts, dropouts and course terminations. The data treatment considered the manual removal of several characteristics that did not add value to the output of the algorithm, resulting in a package composed of 2184 instances. Thus, the logistic regression, MLP and XGBoost models developed and compared could predict a binary output of graduation or non-graduation to each student using 30% of the dataset to test and 70% to train, so that was possible to identify a relationship between the six attributes explored and achieve, with the best model, 94.15% of accuracy on its predictions.