Effect of the Multiple Intelligences in multiclass predictive model of computer programming course achievement

Unified Model for Learning Style Recommendation

“…Most of the studies in the education domain, including computer education, have consistently indicated that students' cognitive ability is correlated strongly with the academic achievement [12]. The academic background (AB) is the traditional cognitive factor recognized as a powerful predictor [13,14]. It is observed in many studies that the ability in mathematics, science [15], and foreign language fluency (for a nonspeaking English country) [16] is a related factor to predict a student's success in a computer programming course.…”

“…It is observed in many studies that the ability in mathematics, science [15], and foreign language fluency (for a nonspeaking English country) [16] is a related factor to predict a student's success in a computer programming course. Multiple intelligence (MI), which is a form of human intellectual competencies, is another cognitive ability that was utilized as a predictor of students learning performance [14,17]. Results of the previous studies showed that the word smart (linguistic intelligence) and numberreasoning smart (logical-mathematical intelligence) represent the scholarly intelligence [14,17].…”

“…Multiple intelligence (MI), which is a form of human intellectual competencies, is another cognitive ability that was utilized as a predictor of students learning performance [14,17]. Results of the previous studies showed that the word smart (linguistic intelligence) and numberreasoning smart (logical-mathematical intelligence) represent the scholarly intelligence [14,17]. In the areas of mathematics and computer education, number-reasoning smart and picture smart (spatial intelligence) were reported as the most important intelligence for studying computer programming [14,17,18].…”

“…Results of the previous studies showed that the word smart (linguistic intelligence) and numberreasoning smart (logical-mathematical intelligence) represent the scholarly intelligence [14,17]. In the areas of mathematics and computer education, number-reasoning smart and picture smart (spatial intelligence) were reported as the most important intelligence for studying computer programming [14,17,18]. Moreover, it was confirmed that adopting the MI as a copredictor together with the traditional factors can enhance the prediction model's performance [14].…”

“…In the areas of mathematics and computer education, number-reasoning smart and picture smart (spatial intelligence) were reported as the most important intelligence for studying computer programming [14,17,18]. Moreover, it was confirmed that adopting the MI as a copredictor together with the traditional factors can enhance the prediction model's performance [14]. Recently, researchers have designed a set of learning activities based on the MI approach [19] for improving the performance in computer programming of first-year undergraduate students.…”