Although using machine learning for predicting which students are at risk of failing a course is indeed valuable, how can we identify which characteristics of individual students contribute to their being At-Risk? By characterising individual At-Risk students we could potentially advise on specific interventions or ways to reduce their probability of being At-Risk. We propose the use of local model-agnostic and counterfactual explanations to attack this challenge. Local model-agnostic LIME and SHAP methods were critically evaluated in this study. These methods explain why individual students are At-Risk. Based on these local explanations, counterfactual explanations were generated and provide potential ways for the individual student to reduce At-Risk probability. These methods were illustrated on two randomly selected At-Risk students. SHAP was found to be more stable than LIME and suggested for future use. Counterfactual explanations promised much but require the features to be actionable and causal for this method to work effectively. This entire process should be performed under the guidance of experienced educators to benefit the students.