## 1 Introduction  
Student academic performance in higher education (HE) is researched extensively to tackle academic underachievement, increased university dropout rates, graduation delays, among other tenacious challenges [1]. In simple terms, student performance refers to the extent of achieving short-term and long-term goals in education [2]. However, academicians measure student success from different perspectives, ranging from students' final grades, grade point average (GPA), to future job prospects [3]. The literature offers a wealth of computational efforts striving to improve student performance in schools and universities, most notably those driven by data mining and learning analytics techniques [4]. However, confusion still prevails regarding the effectiveness of the existing intelligent techniques and models. The timely prediction of student performance enables the detection of low performing students, thus, empowering educators to intervene early during the learning process and implement the required interventions. Fruitful interventions include, but are not limited to, student advising, performance progress monitoring, intelligent tutoring systems development, and policymaking [5]. This endeavor is strongly boosted by computational advances in data mining and learning analytics [6]. A recent comprehensive survey highlights that approximately 70% of the reviewed work investigated student performance prediction using student grades and GPAs, while only 10% of the studies inspected the prediction of student achievement using learning outcomes [3]. This gap incited us to thoroughly investigate the work carried out where the learning outcomes are used as a proxy for student academic performance. Outcome-based education is a paradigm of education that focuses on implementing and accomplishing the so-called learning outcomes [7]. In effect, student learning outcomes are goals that measure the extent to which students attain the intended competencies, specifically knowledge, skills, and values, at the end of a certain learning process. In our view, the student outcomes represent a more holistic metric for judging student academic achievements than mere assessment grades. This view concurs with the claim that the learning outcomes represent critical factors of student academic success [8]. Moreover, renowned HE accreditation organizations, such as ABET and ACBSP, use the learning outcomes as the building blocks for assessing the quality of educational programs [9]. Such importance calls for more research efforts to predict the attainment of learning outcomes, both at the course and program levels. The lack of systematic surveys investigating the prediction of student performance using student outcomes has motivated us to pursue the objectives of this research. In a systematic literature review (i.e., SLR), a step-by-step protocol is executed to identify, select, and appraise the synthesized studies to answer specific research questions [10; 11]. Our systematic survey aims to review the research works conducted in this field between 2010 and 2020 to:  
* Deeply understand the intelligent approaches and techniques developed to forecast student learning outcomes, which represent the student academic performance. * Compare the performance of existing models and techniques on different aspects, including their accuracy, strengths, and weaknesses. * Specify the dominant predictors (e.g., factors and features) of student learning outcomes based on evidence from the synthesis. * Identify the research challenges and limitations facing the current intelligent techniques for predicting academic performance using learning outcomes. * Highlight future research areas to ameliorate the prediction of student performance using learning outcomes. The remainder of this paper is organized into eight sections. Section 2 presents the foundational concepts of student performance prediction and highlights the surveys conducted in this field regarding their shortcomings.