Bayesian Knowledge Tracing for Navigation through Marzano’s Taxonomy Navarro Perales, Joaquin; Fuente Valentín, Luis de la; Cervantes Pérez, Francisco ...
International journal of interactive multimedia and artificial intelligence,
06/2021, Letnik:
6, Številka:
6
Journal Article
Odprti dostop
In this paper we propose a theoretical model of an ITS (Intelligent Tutoring Systems) capable of improving and updating computer-aided navigation based on Bloom's taxonomy. For this we use the ...Bayesian Knowledge Tracing algorithm, performing an adaptive control of the navigation among different levels of cognition in online courses. These levels are defined by a taxonomy of educational objectives with a hierarchical order in terms of the control that some processes have over others, called Marzano's Taxonomy, that takes into account the metacognitive system, responsible for the creation of goals as well as strategies to fulfill them. The main improvements of this proposal are: 1) An adaptive transition between individual assessment questions determined by levels of cognition. 2) A student model based on the initial response of a group of learners which is then adjusted to the ability of each learner. 3) The promotion of metacognitive skills such as goal setting and self-monitoring through the estimation of attempts required to pass the levels. One level of Marzano's taxonomy was left in the hands of the human teacher, clarifying that a differentiation must be made between the tasks in which an ITS can be an important aid and in which it would be more difficult. KEYWORDS Bayesian Knowledge Tracing, Bloom's Taxonomy, Computer-Assisted Instruction, Intelligent Tutoring System, Marzano's Taxonomy.
This research was aimed at profiling student’s thinking skills in dealing with Higher-Order Thinking Skills (HOTS) questions based on Marzano taxonomy by referring to 13 indicators. This ...pre-experimental research employed pretest-posttest design. The indicators included were comparison, classification, deductive reasoning, inductive reasoning, error analysis, construction, analysis perspective, abstraction, decision making, investigation, problem solving, inquiry experiment, and innovation finding. The instrument used was a 13-essay question (r-Pearson= 0.79 and Cronbach alpha = 0.68). The data gained from 98 students of Natural Science Education was then analyzed using paired t-test. The results showed the significant different between pre-test and posttest (sig. <0.01). As many as four HOTS indicators (i.e. deductive reasoning, error analysis, construction, and abstraction) were categorized as low level. Meanwhile, the eighth HOTS indicators were categorized as moderate level, namely: comparison, inductive reasoning, analysis perspective, decision making, investigation, solving problem, inquiry experiment, and innovative finding. In addition, the classification indicator was considered in high level in the end of the course. It can be concluded that, in general, students’ HOTS are still in moderate level. Thus, the proper strategies should be designed to improve this condition into the optimal level.
Research indicates that concern is often expressed about the language and discourse skills that new students bring with them when they first enrol at university, which leads to assumptions being made ...about their academic abilities. In this paper, an argument is developed, through detailed analysis of student writing, that many new first-year students have nascent higher order thinking skills and the potential to be successful in their studies. The work of Robert Marzano and his associates is applied to student writing. Author abstract
PDF
