The Programme for the International Assessment of Adult Competencies (PIAAC) assesses adults’ literacy, numeracy, and problem-solving competencies in functional situations. However, PIAAC-oriented assessment resources that reflect Indonesian adult contexts remain limited. This study aimed to design and evaluate a contextualized PIAAC assessment environment for Indonesian adults. Educational Design Research was employed using Plomp’s preliminary research, prototyping, and assessment phases, integrated with formative evaluation through focus group discussions, one-to-one evaluations, small-group evaluations, and field testing. The study produced 93 contextualized items comprising 32 literacy, 31 numeracy, and 30 problem-solving items, which were incorporated into a computer-based testing system and disseminated through an item book and social media content. Iterative evaluation identified and addressed issues related to contextual relevance, competency alignment, instructional clarity, response-format flexibility, and technical functionality. A field test involving 30 teachers showed mean achievement percentages of 84.33% in literacy, 80.33% in problem solving, and 74.33% in numeracy. The findings indicate that international adult competency constructs can be operationalized within authentic Indonesian contexts and iteratively transformed into a practical, usable digital assessment environment. The study offers design-based guidance for developing culturally relevant adult competency assessments while maintaining alignment with the PIAAC framework.
This study explores the ethnomathematical dimensions embedded in the rice-farming practices of the Wae Nakeng community within the Lingko Lodok system of Manggarai, Indonesia. Grounded in the premise that local cultural practices contain rich mathematical knowledge, the research aims to identify mathematical activities and school mathematics concepts embedded in field structure, farming stages, and the use of traditional tools and measurement systems. The study employed a qualitative ethnographic approach, collecting data through in-depth interviews, participant observations, and field documentation. The data were analyzed using an interactive model encompassing data reduction, data display, and conclusion drawing, with the coding process guided by Bishop’s framework of mathematical activities. The findings reveal two major results. First, the farming practices demonstrate key mathematical activities, including counting, measuring, designing, locating, and explaining, as reflected in land division, seed preparation, planting patterns, irrigation management, and harvesting processes. Second, these activities embody various school mathematics concepts, such as arithmetic operations, ratio and proportional reasoning, linear functions, arithmetic sequences, geometric shapes and transformations, and standard and non-standard measurement. Overall, this study highlights the potential of Lingko Lodok-based ethnomathematics to inform meaningful mathematics instruction that bridges local knowledge with formal school curricula.
Constructing a valid geometric proof requires prospective mathematics teachers to coordinate premises, deductive warrants, proof goals, and representations. Although empirical studies have documented multiple proof-related difficulties, less is known about how a breakdown at one point constrains subsequent reasoning. This qualitative descriptive-exploratory study examined four direct proof tasks and task-based semi-structured interviews with six prospective mathematics teachers from an initial sample of 20. Data were analyzed using an adapted Explorative Mathematical Argumentation (EMA) framework composed of initiation, tools, goals, and modalities. A response was classified as a failure when both written and interview data indicated that the participant possessed relevant knowledge but did not use or apply it appropriately. Failures were present in all components of the EMA framework, and were most prevalent in the application of theorems and in the use of deductive logic. Within-participant analysis indicated possible pathways, as the under-integration of a premise limited access to an appropriate warrant; the use of a theorem without support resulted in invalid intermediate assertions, and disrupted the continuity of the argument; and the use of a theorem along with a drawing resulted in a lack of checking the theorem against the conditions. These pathways were not rigid but indicated the interplay of different conceptual, strategic, representational, and communicative demands. The results of the study provide support for a stage-differentiated and functionally interrelated description of proof, and for the construction of pathways of failure as a process-oriented description of gaps across different classifications of failure.
Teachers' beliefs significantly influence the efficacy of mathematics education. The absence of an instrument to assess beliefs about deep learning poses a challenge for evaluating and enhancing mathematics teachers' competence in adopting deep learning. This research aims to develop and test the validity and reliability of an instrument to measure mathematics teachers' beliefs about implementing deep learning. This research involved 108 mathematics teachers from five provinces in Indonesia. The research data were collected using a questionnaire with a three-dimensional scale: mindful learning, meaningful learning, and joyful learning. The collected research data were then analyzed using the Rasch model, assisted by Winstep software version 3.73. The results of the first calibration indicated that the instrument's quality was very good, and the respondents' answers were quite consistent. The analysis was continued by examining each person and item more closely, resulting in the identification of three items as misfits. The second calibration was carried out by eliminating the misfit statement items. In the second calibration, a valid, reliable, and unidimensional instrument was obtained, which did not exhibit bias towards any of the research attributes. The instrument demonstrated very good reliability, and respondents' answers were quite consistent. After repeated calibration, a deep learning belief measurement instrument was obtained, consisting of 17 high-quality items with adequate psychometric properties.
The study examined the effect of neuroeducational methodologies supported by artificial intelligence on logical-mathematical thinking in teachers in training in Basic General Education at the Technical University of Ambato, Ecuador. A quantitative approach was used with a quasi-experimental, longitudinal pre-test-posttest design with non-equivalent groups. The sample was made up of 480 students, distributed in a control group that received mathematics teaching supported by conventional ICT and an experimental group that participated in a 14-week intervention based on neuroeducational methodologies supported by artificial intelligence. Data were collected through a 21-item performance test that evaluated logical-relational reasoning, abstraction and mathematical representation, and strategic problem solving. The internal consistency of the instrument was adequate (Cronbach's alpha = 0.816; McDonald's omega = 0.817). The analysis included descriptive statistics, ANCOVA, and linear regression in Jamovi. After controlling for pre-test scores, the ANCOVA showed a significant effect of group on post-test logical-mathematical thinking F (1,477) = 206, p < 0.001, η²p = 0.302. Linear regression indicated that belonging to the experimental group significantly predicted final performance (B= 0.810, β= 0.882, t= 49.0, p < 0.001). The results suggest that neurodidactic methodologies structured and supported by artificial intelligence can strengthen logical-mathematical thinking in initial teacher training.
