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Perhaps more than any other subject, teaching and learning mathematics depends on language. Mathematics is about relationships: relation between numbers, categories, geometric forms, variables and so on. In general, these relationships are abstract in nature and can only be realized and articulated through language. Even mathematical symbols must be interpreted linguistically. Thus, while mathematics is often seen as language free, in many ways learning mathematics fundamentally depends on language. For students still developing their proficiency in the language instruction, the challenge is considerable. Indeed research has shown that while many second speakers of English (L2) students are quickly able to develop a basic level of conversational English it takes several years do develop more specialised mathematical English. This paper reports findings of a study whose part of the objectives investigated how students construe specialised mathematical meanings from everyday words to express conceptual understanding of mathematics. The study employed multiple-case study design in three categories of schools, that is, Sub-County School (SCS), County School (CS) and Extra-County School (ECS). Data were collected by questionnaires, classroom observations and interviews. Findings indicate that students had challenges in interpreting mathematical meanings of ordinary vocabulary used in mathematics curriculum-they stated ordinary meanings of words instead of mathematical meanings. The paper recommends integration of mathematical language as a strand in the curriculum of mathematics in secondary schools in L2 context to assist learners attain conceptual understanding of mathematics.

The tremendous developments in technology that have been realized in this digital era have greatly improved the way in which data is collected and used in schools. Over the years the number of secondary schools using technology in processing student data has been increasing steadily. As a result, a large amount of data in electronic form has been gathered. Classification algorithms can be used to study the patterns presented in these data and use it to predict a suitable career for a student. In this study classification algorithms were used to predict a suitable career for form four students. The study evaluated the best classification algorithm for implementing the career recommendation system in Kenya. The Cross Industry Standard Process for Data Mining framework was applied to a dataset drawn from form four students in Bungoma County in Kenya. Stratified random sampling was used to select 50 secondary schools and a 10% of candidates were selected from every sampled schools. The collected data were cleansed, preprocessed and analyzed using a data mining tool of RapidMiner. Various classification algorithms were evaluated in predicting a suitable career for a student. The study findings revealed that classification algorithms can be used to predict a suitable career for a student. All the classifiers that were used gave a predictive accuracy of above 88% though deep learning was the most accurate with 97.5%. However, since the classifiers out performed each other in various metrics, therefore using multiple classification algorithms in building the recommendation model can yield better results. The study therefore concludes that classification models comprising of multiple classifiers can be used to predict suitable careers for secondary students.

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